A PROCESSING METHOD, DEVICE AND ELECTRONIC DEVICE FOR A QUESTION-AND-ANSWER STATEMENT

METHODE DE TRAITEMENT, DISPOSITIF ET DISPOSITIF ELECTRONIQUE POUR UN ENONCE DE QUESTION ET DE REPONSE

English Abstract

The present application discloses a questioning/answering (hereinafter referred to as "Q&A") statement processing method, and corresponding device and electronic equipment, of which the method comprises splitting the session record into corresponding Q&A groups according to a preset Q&A splitting rule, wherein the Q&A groups include at least one questioning statement and at least one answering statement; determining a processing rule to which the Q&A groups correspond; splitting the Q&A groups into corresponding statement pairs according to the processing rule; and updating a knowledge base of a Q&A system according to the statement pairs. The present application achieves update of the knowledge base of the Q&A system according to historical Q&A records, and solves prior-art problems that questioning statements and answering statements included in human session data cannot be analyzed and mined, whereby update of the knowledge base is slow, and success rate of response is adversely affected.

French Abstract

La présente demande concerne un procédé de traitement dinstruction lié à des questions/réponses (ci-après dénommé « questions-réponses »), ainsi quun dispositif et un matériel électronique correspondants. Le procédé comprend la division de lenregistrement de session en des groupes de questions-réponses correspondants (chaque groupe comprenant au moins une déclaration de questionnement et une déclaration de réponse), létablissement dune règle de traitement à laquelle les groupes de questions-réponses correspondent, la division des groupes de questions-réponses en des paires de déclaration correspondantes (en fonction de la règle de traitement) et la mise à jour dune base de connaissances dun système de questions-réponses, en fonction des paires de déclaration. La présente demande réalise une mise à jour de la base de connaissances du système de questions-réponses en fonction danciens dossiers de questions-réponses et règle des problèmes antérieurs que les déclarations dinterrogation et les déclarations de réponse contenues dans les données des sessions humaines ne peuvent pas être analysées et extraites, ce qui ralentit la mise à jour de la base de connaissances et nuit au taux de réussite de la réponse.

Claims

Claims:
1. A computer device comprising:
an obtaining module, configured to obtain a session record, wherein the
session record
includes at least two statements, wherein the statements include questioning
statements
sent by questioners and answering statements sent by answerers;
a splitting module, configured to:
split the session record into corresponding groups according to a preset
splitting rule, wherein groups include at least one questioning statement and
at least one answering statement;
split the groups into corresponding statement pairs according to a processing
rule to which the groups correspond;
use, when a number of the answering statements included in the group does
not exceed a first preset threshold and a number of the questioning statements

as included exceeds the first preset threshold, a preset binary classifier to
predict whether the questioning statements as included and an antecedent
questioning statements of the questioning statements as included belong to a
same question;
a judging module, configured to determine the processing rule to which the
groups
correspond according to the number of the questioning statements and the
number of
the answering statements included in the groups, wherein the processing rule
is based
on the number of questioning statements and the number of answering statements
as
compared to the first preset threshold; and
an updating module, configured to update a knowledge base of a system
according to
statement pairs.
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2. The computer device of claim 1, wherein each statement has a
corresponding generation
time.
3. The computer device of claim 2, wherein the splitting module further
comprises:
sequentially traversing the session record according to generation time of
each
statement;
judging, when the statement traversed is the questioning statement, whether a
traversed
questioning statement and an antecedent questioning statement of the traversed

questioning statement belong to same group according to a sentence pattern of
antecedent answering statement of the traversed questioning statement and/or
according
to an interval time to the antecedent questioning statement of the traversed
questioning
statement; and
determining, when the statement traversed is the answering statement, that a
traversed
answering statement belongs to the group to which the antecedent questioning
statement
of the traversed answering statement corresponds.
4. The computer device of claim 3, wherein the splitting module further
comprises:
splitting, when the number of the questioning statements included in the group
does not
exceed the first preset threshold, the questioning statements each into at
least two text
segments according to preset signs included in the questioning statements;
predicting whether two adjacent text segments belong to a same question by
employing
a preset binary classifier;
generating corresponding questioning statements respectively according to text

segments predicted to belong to the same question; and
generating the corresponding statement pairs according to all the questioning
statements
as generated and the answering statements included in the group.
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5. The computer device of claim 4, further comprises:
traversing the text segments, and merging traversed text segments with
corresponding
posterior text segments when number of characters of the traversed text
segments is
smaller than a second preset threshold.
6. The computer device of claim 5, further comprises:
merging the traversed text segments with corresponding posterior text segments
by
employing a preset classifier algorithm when the traversed text segments and
the
corresponding posterior text segments belong to a same intent class or when
the
traversed text segments belong to a preset merging intent class.
7. The computer device of claim 6, wherein the splitting module further
comprises:
merging, when there are the questioning statements that belong to the same
question,
the questioning statements that belong to the same question and generating the

corresponding statement pairs according to all merged questioning statements
and the
answering statements; and
generating the corresponding statement pairs according to all the questioning
statements
and the answering statements included in the group, when there are no
questioning
statements that belong to the same question.
8. The computer device of claim 7, wherein the splitting module further
comprises:
combining, when the numbers of the questioning statements and the answering
statements included in the group both exceed the first preset threshold, the
questioning
statements and the answering statements included in the group; and
generating the corresponding statement pairs.
9. The computer device of claim 8, wherein the updating module further
comprises:
clustering the statement pairs by using a preset clustering algorithm;
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generating statement pair groups;
determining the number of the questioning statements included in each
statement pair
group;
determining matching degrees between the questioning statements and the
answering
statements included in the statement pair groups according to a preset
similarity
algorithm;
determining a weight to each statement pair group corresponds according to
corresponding matching degrees and number of questioning statements included
in the
statement pair groups; and
sequentially updating the knowledge base of the system according to the weight
to
which each statement pair group corresponds.
10. The computer device of claim 9, the splitting module further comprises:
rectifying any wrong word included in the session record according to a preset

rectifying rule; and
performing a normalizing process on rectified session record.
11. The computer device of claim 10, the splitting module further comprises:
recognizing an intent class to which each questioning statement included in
the session
record corresponds by employing the preset classifier algorithm and
eliminating any
questioning statement to which a preset irrelevant intent class corresponds as
included
in the session record.
12. The computer device of claim 11, further comprises a process of analyzing
and mining
dialogue statements between customer service and a user comprising:
obtaining a session record to be processed and preprocessing obtained session
record,
and
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rectifying any wrong word included in the session record according to a preset

rectifying rule.
13. The computer device of claim 12, further comprises:
performing a purification operation on all characters included in the session
record;
recognizing a dialogue intent to which the questioning statement sent by each
user
corresponds by using the preset classifier algorithm;
14. The computer device of claim 13, further comprises:
traversing a dialogue according to a temporal sequence of generation times;
screening questioning statements to be processed of the user when the
questioning
statements to be processed are traversed, and eliminating any questioning
statement to
be processed of the user does not conform to a preset condition;
determining a questioning statement to be processed is to be merged with the
antecedent
questioning statement of the questioning statement to be processed according
to a preset
merging rule;
eliminating any answering statement whose number of characters is smaller than
a
preset number threshold when the answering statements of the customer service
are
traversed, and determining any answering statement remaining after the
elimination as
an answering statement to be processed;
merging the answering statement to be processed with an antecedent answering
statement when the antecedent statement of the answering statement to be
processed is
an answering statement of the customer service, and storing the merged
answering
statement to the group to which the antecedent questioning statement
corresponds; and
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merging the answering statement to be processed in the group to which the
questioning
statement corresponds when the antecedent statement of the answering statement
to be
processed is a questioning statement of the user.
15. The computer device of claim 14, further comprises
splitting the questioning statements included in the group each into text
segments when
the group is of QA type;
processing the text segments from front to back, and merging any text segment
whose
number of characters is smaller than the preset number threshold in a
posterior text
segment of this text segment; and/or
merging any text segment pertaining to the same intent class as the
corresponding
posterior text segment or pertaining to a preset merging intent class in the
posterior text
segment of this text segment;
sequentially obtaining a preset number of adjacent text segments through a
sliding
window, and predicting obtained text segments belong to a same and single
question by
means of a binary classifier algorithm;
traversing the questioning statements included in the group and judging each
questioning statement and its antecedent questioning statement belong to the
same
question when the group is of QQA type; and
combining all questioning statements and answering statements in pairs to
generate
corresponding OA statement pairs when the group is of QAQA type.
16. The computer device of claim 15, further comprises:
clustering the statements by employing a preset clustering algorithm,
generating
statement pair groups, and determining the number of questioning statements
included
in each statement pair group;
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determining matching degrees between the questioning statements and the
answering
statements included in the statement pair groups according to a preset
similarity
algorithm; and
determining a weight to which each statement pair group corresponds according
to the
corresponding matching degrees and the numbers of questioning statements
included in
the statement pair groups.
17. The computer device of claim 16, wherein the session record is directed to
text statements,
principal wrong words are homonyms.
18. The computer device of claim 17, wherein the session record is directed to
a speech
statements, the session record is firstly required to convert the speech
statements into the
text statements through speech recognition technique.
19. The computer device of claim 18, wherein language model and word frequency
features
are combined.
20. The computer device of claim 19, wherein corresponding rectifying rules
are provided for
the speech statements and the text statements respectively.
21. The computer device of claim 20, wherein a wrong words are rectified
according to the
corresponding rectifying rules.
22. The computer device of claim 21, wherein the purification operation
includes removing
irrelevant characters including preset useless punctuations and preset stop
words,
recognizing irrelevant information contained in each text statement including
commodity
names and placenames and normalizing the irrelevant infoimation to
corresponding preset
characters according to which the irrelevant information corresponds.
23. The computer device of claim 22, the session record of the user with
customer service
within one day is a segment of the dialogue.
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24. The computer device of claim 23, wherein the session record is split into
one or more
dialogues, and the dialogues are split into groups.
25. The computer device of claim 24, wherein the user consults same type of
questions within
a preset period of time, wherein the customer service has replied, the user
consults
different questions next time in the dialogue with the customer service.
26. The computer device of claim 25, wherein to eliminate any questioning
statement with
eliminable intent and irrelevant to business whose number of characters is
smaller than a
preset number threshold or intent is judged by the preset classifier algorithm
as chitchat
intent.
27. The computer device of claim 26, wherein the interval time between the
antecedent
questioning statement of the questioning statement to be processed and the
questioning
statement to be processed exceeds a corresponding preset time threshold and/or
when the
sentence pattern of the antecedent answering statement of the questioning
statement to be
processed is a preset sentence pattern, the questioning statement to be
processed is merged
with its antecedent questioning statement.
28. The computer device of claim 27, wherein the antecedent questioning
statement is a
questioning statement that is temporally antecedent to the statement to be
processed and
with a shortest interval time to the statement to be processed.
29. The computer device of claim 28, wherein the antecedent answering
statement is an
answering statement that is temporally antecedent to the statement to be
processed and
with the shortest interval time to the statement to be processed.
30. The computer device of claim 29, wherein the interval time between the
antecedent
questioning statement of the questioning statement to be processed and the
questioning
statement to be processed exceeds the corresponding preset time threshold,
judge there is
no relevancy between the antecedent questioning statement and the questioning
statement
to be processed, so a new group is generated according to the questioning
statement to be
processed.
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31. The computer device of claim 30, wherein the corresponding preset time
threshold is not
exceeded, judge there is relevancy between the antecedent questioning
statement and the
questioning statement to be processed, and the questioning statement to be
processed is
merged in the group to which the antecedent questioning statement corresponds.
32. The computer device of claim 31, wherein the preset sentence patterns
include statements
that guide the user to further respond to responses made by the customer
service, includes
asks in reply by the customer service to indefinite expressions of users, or
the sentence
pattern asking for essential information from the user.
33. The computer device of claim 32, wherein the antecedent answering
statement is of the
preset sentence pattern, the statement sent by the user after the antecedent
answering
statement is made in reply to this antecedent answering statement and is
relevant to the
antecedent answering statement, and the questioning statement to be processed
is merged
with the antecedent questioning statement.
34. The computer device of claim 33, wherein the antecedent answering
statement of the
questioning statement to be processed is of the preset sentence pattern, the
questioning
statement to be processed is merged with the antecedent questioning statement,
and the
questioning statement to be processed is merged in the group to which the
antecedent
questioning statement corresponds.
35. The computer device of claim 34, wherein the antecedent statement is a
statement that is
temporally antecedent to the statement to be processed and with the shortest
interval time
to the statement to be processed.
36. The computer device of claim 35, wherein splitting the dialogue into
groups, wherein
result includes three types of groups comprising:
one question corresponds to a segment of reply, is marked as QA;
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plural questions correspond to a segment of reply, wherein the user asks
plural
questions and the customer service replies with a segment of words, is marked
as QQA;
and
plural questions correspond to plural replies, wherein several rounds of
communication
are carried out between the user and the customer service in a short time, is
marked as
QAQA.
37. The computer device of claim 36, wherein corresponding type is determined
according to
number of answering statements and questioning statements included in each
group, and is
processed according to corresponding processing rule.
38. The computer device of claim 37, wherein QA is a standard input form of a
algorithm,
wherein one standard question is only meant to express one question in the
knowledge
base.
39. The computer device of claim 38, wherein an auxiliary algorithm is
required during
splitting to judge whether two segments of words are directed to one question
or to two
questions.
40. The computer device of claim 39, wherein the auxiliary algorithm is a
binary classifier,
wherein inputs to the binary classifier are two statements.
41. The computer device of claim 40, wherein any model realizes binary
questions.
42. The computer device of claim 41, wherein model bert predicts during the
process of
pretraining whether input two statements are directed to context of the same
and single
statement or topics irrelevant to each other, serves as the classifier, and
fine-tuning training
is performed.
43. The computer device of claim 42, wherein the posterior text segment
indicates a text
segment following and immediately adjacent to the text segment being
processed.
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44. The computer device of claim 43, wherein the classifier algorithm merges
any text
segment judged as pertaining to the same intent class as the posterior text
segment or
pertaining to the preset merging intent class as a chitchat class in the
posterior text
segment.
45. The computer device of claim 44, wherein text segments predicted to belong
to the same
question is merged into one questioning statement.
46. The computer device of claim 45, wherein the text segments predicted to
belong to
different questions are split into two different questioning statements.
47. The computer device of claim 46, wherein the groups of QA type not
belonging to the
same and single question are converted to groups of QQA type, wherein the
groups of QA
type whose all text segments belong to the same and single question are split
into a QA
statement pair only includes one questioning statement and one answering
statement.
48. The computer device of claim 47, wherein to recognize to which
circumstance a group of
QQA type specifically pertains, judge through a binary classification
algorithm.
49. The computer device of claim 48, wherein the questioning statement is
split into text
segments, and any text segment whose number of characters included is smaller
than the
preset number threshold or pertaining to the preset merging intent is directly
merged with
the antecedent questioning statement, or the questioning statement and the
antecedent
questioning statement are input together in the binary classification
algorithm to judge they
belong to the same question.
50. The computer device of claim 49, wherein it is judged any text segment and
corresponding
antecedent questioning statement belong to the same question, the text
statement and the
corresponding antecedent questioning statement are merged into one questioning

statement.
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51. The computer device of claim 50, wherein it is recognized any text
segment and the
corresponding antecedent questioning statement do not belong to the same
question, the
questioning statement is split into new questioning statements.
52. The computer device of claim 51, wherein the statements remain are only
one answering
statement and one questioning statement, they are determined as the QA
statement pair.
53. The computer device of claim 52, wherein the statements remain are more
than one
questioning statement and one answering statement, the questioning statements
and the
answering statements are combined in pairs to generate corresponding QA
statement pairs.
54. The computer device of claim 53, wherein interaction between the user and
the customer
service in the short time is split into plural groups of QA statement pairs.
55. The computer device of claim 54, wherein the answering statements and the
questioning
statements are directly combined in pairs with respect to groups of QAQA type.
56. The computer device of claim 55, wherein clustering is to incorporate
similar questions
together to constitute a cluster.
57. The computer device of claim 56, wherein calculate text distance metrics
amongst the
statement pairs via a text matching algorithm and determine the statement
pairs belong to
same statement pair group according to the text distance metrics.
58. The computer device of claim 57, wherein the text matching algorithm is an
algorithm
calculates similarity degree of two texts.
59. The computer device of claim 58, wherein an unsupervised text matching
algorithm, word
mover's distance (WMD), is used.
60. The computer device of claim 59, wherein any clustering algorithm is
applied to determine
statement pairs belong to the same statement pair group.
61. The computer device of claim 60, wherein in all QA pairs, there are
invalid QA pairs
caused by imprecise splitting, and circumstance in which answers are not
pertinent to
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questions asked due to negligence of the customer service, wherein invalid QA
statement
pairs are removed.
62. The computer device of claim 61, wherein filtration of the QA statement
pairs is decided
by matching degrees of questions and answers, wherein the QA statement pairs
whose
matching degrees between questioning statements and answering statements
satisfy a
preset condition remain, wherein the QA statement pairs whose matching degrees
between
questioning statements and answering statements do not satisfy are eliminated
and filtered
out.
63. The computer device of claim 62, wherein matching process is a text
matching process,
wherein a set of supervised algorithms are trained based on existing knowledge
base data
to perform similarity calculation.
64. The computer device of claim 63, wherein frequently asked questions have
higher
priorities to be maintained in the knowledge base.
65. The computer device of claim 64, wherein more important questions are
preferentially
maintained wherein some less valuable questions are neglected.
66. The computer device of claim 65, wherein frequencies by which questions
are asked are
measured by the number of questions under each cluster obtained.
67. The computer device of claim 66, wherein accuracy of answers are measured
by matching
degrees of questions and answers in a filtering process.
68. The computer device of claim 67, wherein the corresponding sorting weight
is derived by
normalizing two values and weighting and accumulating he two values.
69. The computer device of claim 68, wherein corresponding statement pairs are
sequentially
obtained according to sorting weights during subsequent maintenance of the
knowledge
base, and screened and processed manually or by machine, and maintained in the

knowledge base.
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70. A system comprising:
an obtaining module, configured to obtain a session record, wherein the
session record
includes at least two statements, wherein the statements include questioning
statements
sent by questioners and answering statements sent by answerers;
a splitting module, configured to:
split the session record into corresponding groups according to a preset
splitting rule, wherein groups include at least one questioning statement and
at least one answering statement;
split the groups into corresponding statement pairs according to a processing
rule to which the groups correspond;
use, when a number of the answering statements included in the group does
not exceed a first preset threshold and a number of the questioning statements

as included exceeds the first preset threshold, a preset binary classifier to
predict whether the questioning statements as included and an antecedent
questioning statements of the questioning statements as included belong to a
same question;
a judging module, configured to determine the processing rule to which the
groups
correspond according to the number of the questioning statements and the
number of
the answering statements included in the groups, wherein the processing nrle
is based
on the number of questioning statements and the number of answering statements
as
compared to the first preset threshold; and
an updating module, configured to update a knowledge base of a system
according to
statement pairs.
71. The system of claim 70, wherein each statement has a corresponding
generation time.
72. The system of claim 71, wherein the splitting module further comprises:
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sequentially traversing the session record according to generation time of
each
statement;
judging, when the statement traversed is the questioning statement, whether a
traversed
questioning statement and an antecedent questioning statement of the traversed

questioning statement belong to same group according to a sentence pattern of
antecedent answering statement of the traversed questioning statement and/or
according
to an interval time to the antecedent questioning statement of the traversed
questioning
statement; and
determining, when the statement traversed is the answering statement, that a
traversed
answering statement belongs to the group to which the antecedent questioning
statement
of the traversed answering statement corresponds.
73. The system of claim 72, wherein the splitting module further comprises:
splitting, when the number of the questioning statements included in the group
does not
exceed the first preset threshold, the questioning statements each into at
least two text
segments according to preset signs included in the questioning statements;
predicting whether two adjacent text segments belong to a same question by
employing
a preset binary classifier;
generating corresponding questioning statements respectively according to text

segments predicted to belong to the same question; and
generating the corresponding statement pairs according to all the questioning
statements
as generated and the answering statements included in the group.
74. The system of claim 73, further comprises:
traversing the text segments, and merging traversed text segments with
corresponding
posterior text segments when number of characters of the traversed text
segments is
smaller than a second preset threshold.
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75. The system of claim 74, further comprises:
merging the traversed text segments with corresponding posterior text segments
by
employing a preset classifier algorithm when the traversed text segments and
the
corresponding posterior text segments belong to a same intent class or when
the
traversed text segments belong to a preset merging intent class.
76. The system of claim 75, wherein the splitting module further comprises:
merging, when there are the questioning statements that belong to the same
question,
the questioning statements that belong to the same question and generating the

corresponding statement pairs according to all merged questioning statements
and the
answering statements; and
generating the corresponding statement pairs according to all the questioning
statements
and the answering statements included in the group, when there are no
questioning
statements that belong to the same question.
77. The system of claim 76, wherein the splitting module further comprises:
combining, when the numbers of the questioning statements and the answering
statements included in the group both exceed the first preset threshold, the
questioning
statements and the answering statements included in the group; and
generating the corresponding statement pairs.
78. The system of claim 77, wherein the updating module further comprises:
clustering the statement pairs by using a preset clustering algorithm;
generating statement pair groups;
determining the number of the questioning statements included in each
statement pair
group;
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deterinining matching degrees between the questioning statements and the
answering
statements included in the statement pair groups according to a preset
similarity
algorithm;
deterinining a weight to each statement pair group corresponds according to
corresponding matching degrees and number of questioning statements included
in the
statement pair groups; and
sequentially updating the knowledge base of the system according to the weight
to
which each statement pair group corresponds.
79. The system of claim 78, the splitting module further comprises:
rectifying any wrong word included in the session record according to a preset

rectifying rule; and
performing a normalizing process on rectified session record.
80. The system of claim 79, the splitting module further comprises:
recognizing an intent class to which each questioning statement included in
the session
record corresponds by employing the preset classifier algorithm and
eliminating any
questioning statement to which a preset irrelevant intent class corresponds as
included
in the session record.
81. The system of claim 80, further comprises a process of analyzing and
mining dialogue
statements between customer service and a user comprising:
obtaining a session record to be processed and preprocessing obtained session
record;
and
rectifying any wrong word included in the session record according to a preset

rectifying rule.
82. The system of claim 81, further comprises:
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performing a purification operation on all characters included in the session
record; and
recognizing a dialogue intent to which the questioning statement sent by each
user
corresponds by using the preset classifier algorithm.
83. The system of claim 82, further comprises:
traversing a dialogue according to a temporal sequence of generation times;
screening questioning statements to be processed of the user when the
questioning
statements to be processed are traversed, and eliminating any questioning
statement to
be processed of the user does not conform to a preset condition;
determining a questioning statement to be processed is to be merged with the
antecedent
questioning statement of the questioning statement to be processed according
to a preset
merging rule;
eliminating any answering statement whose number of characters is smaller than
a
preset number threshold when the answering statements of the customer service
are
traversed, and determining any answering statement remaining after the
elimination as
an answering statement to be processed;
merging the answering statement to be processed with an antecedent answering
statement when the antecedent statement of the answering statement to be
processed is
an answering statement of the customer service, and storing the merged
answering
statement to the group to which the antecedent questioning statement
corresponds; and
merging the answering statement to be processed in the group to which the
questioning
statement corresponds when the antecedent statement of the answering statement
to be
processed is a questioning statement of the user.
84. The system of claim 83, further comprises
splitting the questioning statements included in the group each into text
segments when
the group is of QA type;
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processing the text segments from front to back, and merging any text segment
whose
number of characters is smaller than the preset number threshold in a
posterior text
segment of this text segment; and/or
merging any text segment pertaining to the same intent class as the
corresponding
posterior text segment or pertaining to a preset merging intent class in the
posterior text
segment of this text segment;
sequentially obtaining a preset number of adjacent text segments through a
sliding
window, and predicting obtained text segments belong to a same and single
question by
means of a binary classifier algorithm;
traversing the questioning statements included in the group and judging each
questioning statement and its antecedent questioning statement belong to the
same
question when the group is of a QQA type; and
combining all questioning statements and answering statements in pairs to
generate
corresponding OA statement pairs when the group is of a QAQA type.
85. The system of claim 84, further comprises:
clustering the statements by employing a preset clustering algorithm,
generating
statement pair groups, and determining the number of questioning statements
included
in each statement pair group;
determining matching degrees between the questioning statements and the
answering
statements included in the statement pair groups according to a preset
similarity
algorithm; and
determining a weight to which each statement pair group corresponds according
to the
corresponding matching degrees and the numbers of questioning statements
included in
the statement pair groups.
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86. The system of claim 85, wherein the session record is directed to text
statements, principal
wrong words are homonyms.
87. The system of claim 86, wherein the session record is directed to a speech
statements, the
session record is firstly required to convert the speech statements into the
text statements
through speech recognition technique.
88. The system of claim 87, wherein language model and word frequency features
are
combined.
89. The system of claim 88, wherein corresponding rectifying rules are
provided for the
speech statements and the text statements respectively.
90. The system of claim 89, wherein a wrong words are rectified according to
the
corresponding rectifying rules.
91. The system of claim 90, wherein the purification operation includes
removing irrelevant
characters including preset useless punctuations and preset stop words,
recognizing
irrelevant information contained in each text statement including commodity
names and
placenames and normalizing the irrelevant information to corresponding preset
characters
according to which the irrelevant information corresponds.
92. The system of claim 91, the session record of the user with customer
service within one
day is a segment of the dialogue.
93. The system of claim 92, wherein the session record is split into one or
more dialogues, and
the dialogues are split into groups.
94. The system of claim 93, wherein the user consults same type of questions
within a preset
period of time, wherein the customer service has replied, the user consults
different
questions next time in the dialogue with the customer service.
95. The system of claim 94, wherein to eliminate any questioning statement
with eliminable
intent and irrelevant to business whose number of characters is smaller than a
preset
number threshold or intent is judged by the preset classifier algorithm as
chitchat intent.
48
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96. The system of claim 95, wherein the interval time between the antecedent
questioning
statement of the questioning statement to be processed and the questioning
statement to be
processed exceeds a corresponding preset time threshold and/or when the
sentence pattern
of the antecedent answering statement of the questioning statement to be
processed is a
preset sentence pattern, the questioning statement to be processed is merged
with its
antecedent questioning statement.
97. The system of claim 96, wherein the antecedent questioning statement is a
questioning
statement that is temporally antecedent to the statement to be processed and
with a shortest
interval time to the statement to be processed.
98. The system of claim 97, wherein the antecedent answering statement is an
answering
statement that is temporally antecedent to the statement to be processed and
with the
shortest interval time to the statement to be processed.
99. The system of claim 98, wherein the interval time between the antecedent
questioning
statement of the questioning statement to be processed and the questioning
statement to be
processed exceeds the corresponding preset time threshold, judge there is no
relevancy
between the antecedent questioning statement and the questioning statement to
be
processed, so a new group is generated according to the questioning statement
to be
processed.
100. The system of claim 99, wherein the corresponding preset time threshold
is not exceeded,
judge there is relevancy between the antecedent questioning statement and the
questioning
statement to be processed, and the questioning statement to be processed is
merged in the
group to which the antecedent questioning statement corresponds.
101. The system of claim 100, wherein the preset sentence patterns include
statements that
guide the user to further respond to responses made by the customer service,
includes asks
in reply by the customer service to indefinite expressions of users, or the
sentence pattern
asking for essential information from the user.
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102. The system of claim 101, wherein the antecedent answering statement is of
the preset
sentence pattern, the statement sent by the user after the antecedent
answering statement is
made in reply to this antecedent answering statement and is relevant to the
antecedent
answering statement, and the questioning statement to be processed is merged
with the
antecedent questioning statement.
103. The system of claim 102, wherein the antecedent answering statement of
the questioning
statement to be processed is of the preset sentence pattern, the questioning
statement to be
processed is merged with the antecedent questioning statement, and the
questioning
statement to be processed is merged in the group to which the antecedent
questioning
statement corresponds.
104. The system of claim 103, wherein the antecedent statement is a statement
that is
temporally antecedent to the statement to be processed and with the shortest
interval time
to the statement to be processed.
105. The system of claim 104, wherein splitting the dialogue into groups,
wherein result
includes three types of groups comprising:
one question corresponds to a segment of reply, is marked as QA;
plural questions correspond to a segment of reply, wherein the user asks
plural
questions and the customer service replies with a segment of words, is marked
as QQA;
and
plural questions correspond to plural replies, wherein several rounds of
communication
are carried out between the user and the customer service in a short time, is
marked as
QAQA.
106. The system of claim 105, wherein corresponding type is determined
according to
number of answering statements and questioning statements included in each
group, and is
processed according to corresponding processing rule.
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107. The system of claim 106, wherein QA is a standard input form of a
algorithm, wherein one
standard question is only meant to express one question in the knowledge base.
108. The system of claim 107, wherein an auxiliary algorithm is required
during splitting to
judge whether two segments of words are directed to one question or to two
questions.
109. The system of claim 108, wherein the auxiliary algorithm is a binary
classifier, wherein
inputs to the binary classifier are two statements.
110. The system of claim 109, wherein any model realizes binary questions.
111. The system of claim 110, wherein model bert predicts during the process
of pretraining
whether input two statements are directed to context of the same and single
statement or
topics irrelevant to each other, serves as the classifier, and fine-tuning
training is
performed.
112. The system of claim 111, wherein the posterior text segment indicates a
text segment
following and immediately adjacent to the text segment being processed.
113. The system of claim 112, wherein the classifier algorithm merges any text
segment judged
as pertaining to the same intent class as the posterior text segment or
pertaining to the
preset merging intent class as a chitchat class in the posterior text segment.
114. The system of claim 113, wherein text segments predicted to belong to the
same question
is merged into one questioning statement.
115. The system of claim 114, wherein the text segments predicted to belong to
different
questions are split into two different questioning statements.
116. The system of claim 115, wherein the groups of the QA type not belonging
to the same
and single question are converted to groups of the QQA type, wherein the
groups of the
QA type whose all text segments belong to the same and single question are
split into a
QA statement pair only includes one questioning statement and one answering
statement.
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117. The system of claim 116, wherein to recognize to which circumstance a
group of the
QQA type specifically pertains, judge through a binary classification
algorithm.
118. The system of claim 117, wherein the questioning statement is split into
text segments, and
any text segment whose number of characters included is smaller than the
preset number
threshold or pertaining to the preset merging intent is directly merged with
the antecedent
questioning statement, or the questioning statement and the antecedent
questioning
statement are input together in the binary classification algorithm to judge
they belong to
the same question.
119. The system of claim 118, wherein it is judged any text segment and
corresponding
antecedent questioning statement belong to the same question, the text
statement and the
corresponding antecedent questioning statement are merged into one questioning

statement.
120. The system of claim 119, wherein it is recognized any text segment and
the
corresponding antecedent questioning statement do not belong to the same
question, the
questioning statement is split into new questioning statements.
121. The system of claim 120, wherein the statements remain are only one
answering statement
and one questioning statement, they are determined as the QA statement pair.
122. The system of claim 121, wherein the statements remain are more than one
questioning
statement and one answering statement, the questioning statements and the
answering
statements are combined in pairs to generate corresponding QA statement pairs.
123. The system of claim 122, wherein interaction between the user and the
customer service in
the short time is split into plural groups of QA statement pairs.
124. The system of claim 123, wherein the answering statements and the
questioning statements
are directly combined in pairs with respect to groups of the QAQA type.
125. The system of claim 124, wherein clustering is to incorporate similar
questions together to
constitute a cluster.
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126. The system of claim 125, wherein calculate text distance metrics amongst
the statement
pairs via a text matching algorithm and determine the statement pairs belong
to same
statement pair group according to the text distance metrics.
127. The system of claim 126, wherein the text matching algorithm is an
algorithm calculates
similarity degree of two texts.
128. The system of claim 127, wherein an unsupervised text matching algorithm,
word mover's
distance (WMD), is used.
129. The system of claim 128, wherein any clustering algorithm is applied to
detelinine
statement pairs belong to the same statement pair group.
130. The system of claim 129, wherein in all QA pairs, there are invalid QA
pairs caused by
imprecise splitting, and circumstance in which answers are not pertinent to
questions asked
due to negligence of the customer service, wherein invalid QA statement pairs
are
removed.
131. The system of claim 130, wherein filtration of the QA statement pairs is
decided by
matching degrees of questions and answers, wherein the QA statement pairs
whose
matching degrees between questioning statements and answering statements
satisfy a
preset condition remain, wherein the QA statement pairs whose matching degrees
between
questioning statements and answering statements do not satisfy are eliminated
and filtered
out.
132. The system of claim 131, wherein matching process is a text matching
process, wherein a
set of supervised algorithms are trained based on existing knowledge base data
to perform
similarity calculation.
133. The system of claim 132, wherein frequently asked questions have higher
priorities to be
maintained in the knowledge base.
134. The system of claim 133, wherein more important questions are
preferentially maintained
wherein some less valuable questions are neglected.
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135. The system of claim 134, wherein frequencies by which questions are asked
are measured
by the number of questions under each cluster obtained.
136. The system of claim 135, wherein accuracy of answers are measured by
matching degrees
of questions and answers in a filtering process.
137. The system of claim 136, wherein the corresponding sorting weight is
derived by
normalizing two values and weighting and accumulating he two values.
138. The system of claim 137, wherein corresponding statement pairs are
sequentially obtained
according to sorting weights during subsequent maintenance of the knowledge
base, and
screened and processed manually or by machine, and maintained in the knowledge
base.
139.A method comprising:
obtaining a session record, wherein the session record includes at least two
statements,
wherein the statements include questioning statements sent by questioners and
answering statements sent by answerers;
splitting the session record into corresponding groups according to a preset
splitting
rule, wherein groups include at least one questioning statement and at least
one
answering statement;
deteunining a processing rule to which the groups correspond according to
number of
the questioning statements and number of the answering statements included in
the
groups, wherein the processing rule is based on the number of questioning
statements
and the number of answering statements as compared to a first preset
threshold;
splitting the groups into corresponding statement pairs according to the
processing rule
to which the groups correspond;
using, when the number of the answering statements included in the group does
not
exceed the first preset threshold and the number of the questioning statements
as
included exceeds the first preset threshold, a preset binary classifier to
predict whether
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the questioning statements as included and an antecedent questioning
statements of the
questioning statements as included belong to a same question; and
updating a knowledge base of a system according to statement pairs.
140. The method of claim 139, wherein each statement has a corresponding
generation time.
141. The method of claim 140, wherein splitting the session record into
corresponding groups
according to the preset splitting rule comprises:
sequentially traversing the session record according to generation time of
each
statement;
judging, when the statement traversed is the questioning statement, whether a
traversed
questioning statement and an antecedent questioning statement of the traversed

questioning statement belong to same group according to a sentence pattern of
antecedent answering statement of the traversed questioning statement and/or
according
to an interval time to the antecedent questioning statement of the traversed
questioning
statement; and
determining, when the statement traversed is the answering statement, a
traversed
answering statement belongs to the group to which the antecedent questioning
statement
of the traversed answering statement corresponds.
142. The method of claim 141, wherein splitting the groups into the
corresponding statement
pairs according to the processing rule to which the groups correspond
comprises:
splitting, when the number of the questioning statements included in the group
does not
exceed the first preset threshold, the questioning statements each into at
least two text
segments according to preset signs included in the questioning statements;
predicting two adjacent text segments belong to a same question by employing a
preset
binary classifier;
Date Recue/Date Received 202402-06

generating corresponding questioning statements respectively according to text

segments predicted to belong to the same question; and
generating the corresponding statement pairs according to all the questioning
statements
as generated and the answering statements included in the group.
143. The method of claim 142, further comprises:
traversing the text segments, and merging traversed text segments with
corresponding
posterior text segments when number of characters of the traversed text
segments is
smaller than a second preset threshold.
144. The method of claim 143, further comprises:
merging the traversed text segments with corresponding posterior text segments
by
employing a preset classifier algorithm when the traversed text segments and
the
corresponding posterior text segments belong to a same intent class or when
the
traversed text segments belong to a preset merging intent class.
145. The method of claim 144, wherein splitting the groups into the
corresponding statement
pairs according to the processing rule to which the groups correspond
comprises:
combining, when the numbers of the questioning statements and the answering
statements included in the group both exceed the first preset threshold, the
questioning
statements and the answering statements included in the group; and
generating the corresponding statement pairs.
146. The method of claim 145, wherein splitting the groups into the
corresponding statement
pairs according to the processing rule to which the groups correspond
comprises:
merging, when there are the questioning statements belong to the same
question, the
questioning statements belong to the same question and generating the
corresponding
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statement pairs according to all merged questioning statements and the
answering
statements; and
generating the corresponding statement pairs according to all the questioning
statements
and the answering statements included in the group, when there are no
questioning
statements belong to the same question.
147. The method of claim 146, wherein updating the knowledge base of the
system according
to the statement pairs comprises:
clustering the statement pairs by using a preset clustering algorithm;
generating statement pair groups;
determining the number of the questioning statements included in each
statement pair
group;
detemining matching degrees between the questioning statements and the
answering
statements included in the statement pair groups according to a preset
similarity
algorithm;
determining a weight to each statement pair group corresponds according to
corresponding matching degrees and number of questioning statements included
in the
statement pair groups; and
sequentially150 updating the knowledge base of the system according to the
weight to
which each statement pair group corresponds.
148. The method of claim 147, further comprises:
rectifying any wrong word included in the session record according to a preset

rectifying rule; and
performing a normalizing process on rectified session record.
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149. The method of claim 148, further comprises:
recognizing the intent class to which each questioning statement included in
the session
record corresponds by using the preset classifier algorithm and eliminating
any
questioning statement to which a preset irrelevant intent class corresponds as
included
in the session record.
150. The method of claim 149, further comprises a process of analyzing and
mining dialogue
statements between customer service and a user comprising:
obtaining a session record to be processed and preprocessing obtained session
record;
and
rectifying any wrong word included in the session record according to a preset

rectifying rule.
151. The method of claim 150, further comprises:
performing a purification operation on all characters included in the session
record; and
recognizing a dialogue intent to which the questioning statement sent by each
user
corresponds by using the preset classifier algorithm.
152. The method of claim 151, further comprises:
traversing a dialogue according to a temporal sequence of generation times;
screening questioning statements to be processed of the user when the
questioning
statements to be processed are traversed, and eliminating any questioning
statement to
be processed of the user does not conform to a preset condition;
determining a questioning statement to be processed is to be merged with the
antecedent
questioning statement of the questioning statement to be processed according
to a preset
merging rule;
58

eliminating any answering statement whose number of characters is smaller than
a
preset number threshold when the answering statements of the customer service
are
traversed, and determining any answering statement remaining after the
elimination as
an answering statement to be processed;
merging the answering statement to be processed with an antecedent answering
statement when the antecedent statement of the answering statement to be
processed is
an answering statement of the customer service, and storing the merged
answering
statement to the group to which the antecedent questioning statement
corresponds; and
merging the answering statement to be processed in the group to which the
questioning
statement corresponds when the antecedent statement of the answering statement
to be
processed is a questioning statement of the user.
153. The method of claim 152, further comprises
splitting the questioning statements included in the group each into text
segments when
the group is of a QA type;
processing the text segments from front to back, and merging any text segment
whose
number of characters is smaller than the preset number threshold in a
posterior text
segment of this text segment; and/or
merging any text segment pertaining to the same intent class as the
corresponding
posterior text segment or pertaining to a preset merging intent class in the
posterior text
segment of this text segment;
sequentially obtaining a preset number of adjacent text segments through a
sliding
window, and predicting obtained text segments belong to a same and single
question by
means of a binary classifier algorithm;
traversing the questioning statements included in the group and judging each
questioning statement and its antecedent questioning statement belong to the
same
question when the group is of a QQA type; an
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combining all questioning statements and answering statements in pairs to
generate
corresponding OA statement pairs when the group is of a QAQA type.
154. The method of claim 153 further comprises:
clustering the statements by employing a preset clustering algorithm,
generating
statement pair groups, and determining the number of questioning statements
included
in each statement pair group;
determining matching degrees between the questioning statements and the
answering
statements included in the statement pair groups according to a preset
similarity
algorithm; and
detemrining a weight to which each statement pair group corresponds according
to the
corresponding matching degrees and the numbers of questioning statements
included in
the statement pair groups.
155. The method of claim 154, wherein the session record is directed to text
statements,
principal wrong words are homonyms.
156. The method of claim 155, wherein the session record is directed to a
speech statements,
the session record is firstly required to convert the speech statements into
the text
statements through speech recognition technique.
157. The method of claim 156, wherein language model and word frequency
features are
combined.
158. The method of claim 157, wherein corresponding rectifying rules are
provided for the
speech statements and the text statements respectively.
159. The method of claim 158, wherein a wrong words are rectified according to
the
corresponding rectifying rules.
160. The method of claim 159, wherein the purification operation includes
removing irrelevant
characters including preset useless punctuations and preset stop words,
recognizing
Date Recue/Date Received 202402-06

irrelevant information contained in each text statement including commodity
names and
placenames and normalizing the irrelevant information to corresponding preset
characters
according to which the irrelevant information corresponds.
161. The method of claim 160, the session record of the user with customer
service within one
day is a segment of the dialogue.
162. The method of claim 161, wherein the session record is split into one or
more dialogues,
and the dialogues are split into groups.
163. The method of claim 162, wherein the user consults same type of questions
within a preset
period of time, wherein the customer service has replied, the user consults
different
questions next time in the dialogue with the customer service.
164. The method of claim 163, wherein to eliminate any questioning statement
with eliminable
intent and irrelevant to business whose number of characters is smaller than a
preset
number threshold or intent is judged by the preset classifier algorithm as
chitchat intent.
165. The method of claim 164, wherein the interval time between the antecedent
questioning
statement of the questioning statement to be processed and the questioning
statement to be
processed exceeds a corresponding preset time threshold and/or when the
sentence pattern
of the antecedent answering statement of the questioning statement to be
processed is a
preset sentence pattern, the questioning statement to be processed is merged
with its
antecedent questioning statement.
166. The method of claim 165, wherein the antecedent questioning statement is
a questioning
statement that is temporally antecedent to the statement to be processed and
with a shortest
interval time to the statement to be processed.
167. The method of claim 166, wherein the antecedent answering statement is an
answering
statement that is temporally antecedent to the statement to be processed and
with the
shortest interval time to the statement to be processed.
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168. The method of claim 167, wherein the interval time between the antecedent
questioning
statement of the questioning statement to be processed and the questioning
statement to be
processed exceeds the corresponding preset time threshold, judge there is no
relevancy
between the antecedent questioning statement and the questioning statement to
be
processed, so a new group is generated according to the questioning statement
to be
processed.
169. The method of claim 168, wherein the corresponding preset time threshold
is not
exceeded, judge there is relevancy between the antecedent questioning
statement and the
questioning statement to be processed, and the questioning statement to be
processed is
merged in the group to which the antecedent questioning statement corresponds.
170. The method of claim 169, wherein the preset sentence patterns include
statements that
guide the user to further respond to responses made by the customer service,
includes asks
in reply by the customer service to indefinite expressions of users, or the
sentence pattern
asking for essential information from the user.
171. The method of claim 170, wherein the antecedent answering statement is of
the preset
sentence pattern, the statement sent by the user after the antecedent
answering statement is
made in reply to this antecedent answering statement and is relevant to the
antecedent
answering statement, and the questioning statement to be processed is merged
with the
antecedent questioning statement.
172. The method of claim 171, wherein the antecedent answering statement of
the questioning
statement to be processed is of the preset sentence pattern, the questioning
statement to be
processed is merged with the antecedent questioning statement, and the
questioning
statement to be processed is merged in the group to which the antecedent
questioning
statement corresponds.
173. The method of claim 172, wherein the antecedent statement is a statement
that is
temporally antecedent to the statement to be processed and with the shortest
interval time
to the statement to be processed.
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174. The method of claim 173, wherein splitting the dialogue into groups,
wherein result
includes three types of groups comprising:
one question corresponds to a segment of reply, is marked as QA;
plural questions correspond to a segment of reply, wherein the user asks
plural
questions and the customer service replies with a segment of words, is marked
as QQA;
and
plural questions correspond to plural replies, wherein several rounds of
communication
are carried out between the user and the customer service in a short time, is
marked as
QAQA.
175. The method of claim 174, wherein corresponding type is determined
according to
number of answering statements and questioning statements included in each
group, and is
processed according to corresponding processing rule.
176. The method of claim 175, wherein QA is a standard input form of a
algorithm, wherein
one standard question is only meant to express one question in the knowledge
base.
177. The method of claim 176, wherein an auxiliary algorithm is required
during splitting to
judge whether two segments of words are directed to one question or to two
questions.
178. The method of claim 177, wherein the auxiliary algorithm is a binary
classifier, wherein
inputs to the binary classifier are two statements.
179. The method of claim 178, wherein any model realizes binary questions.
180. The method of claim 179, wherein model bed predicts during the process of
pretraining
whether input two statements are directed to context of the same and single
statement or
topics irrelevant to each other, serves as the classifier, and fine-tuning
training is
performed.
181. The method of claim 180, wherein the posterior text segment indicates a
text segment
following and immediately adjacent to the text segment being processed.
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182. The method of claim 181, wherein the classifier algorithm merges any text
segment judged
as pertaining to the same intent class as the posterior text segment or
pertaining to the
preset merging intent class as a chitchat class in the posterior text segment.
183. The method of claim 182, wherein text segments predicted to belong to the
same question
is merged into one questioning statement.
184. The method of claim 183, wherein the text segments predicted to belong to
different
questions are split into two different questioning statements.
185. The method of claim 184, wherein the groups of the QA type not belonging
to the same
and single question are converted to groups of the QQA type, wherein the
groups of the
QA type whose all text segments belong to the same and single question are
split into a
QA statement pair only includes one questioning statement and one answering
statement.
186. The method of claim 185, wherein to recognize to which circumstance a
group of the
QQA type specifically pertains, judge through a binary classification
algorithm.
187. The method of claim 186, wherein the questioning statement is split into
text segments,
and any text segment whose number of characters included is smaller than the
preset
number threshold or pertaining to the preset merging intent is directly merged
with the
antecedent questioning statement, or the questioning statement and the
antecedent
questioning statement are input together in the binary classification
algorithm to judge they
belong to the same question.
188. The method of claim 187, wherein it is judged any text segment and
corresponding
antecedent questioning statement belong to the same question, the text
statement and the
corresponding antecedent questioning statement are merged into one questioning

statement.
189. The method of claim 188, wherein it is recognized any text segment and
the
corresponding antecedent questioning statement do not belong to the same
question, the
questioning statement is split into new questioning statements.
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190. The method of claim 189, wherein the statements remain are only one
answering statement
and one questioning statement, they are determined as the QA statement pair.
191. The method of claim 190, wherein the statements remain are more than one
questioning
statement and one answering statement, the questioning statements and the
answering
statements are combined in pairs to generate corresponding QA statement pairs.
192. The method of claim 191, wherein interaction between the user and the
customer service
in the short time is split into plural groups of QA statement pairs.
193. The method of claim 192, wherein the answering statements and the
questioning
statements are directly combined in pairs with respect to groups of the QAQA
type.
194. The method of claim 193, wherein clustering is to incorporate similar
questions together to
constitute a cluster.
195. The method of claim 194, wherein calculate text distance metrics amongst
the statement
pairs via a text matching algorithm and determine the statement pairs belong
to same
statement pair group according to the text distance metrics.
196. The method of claim 195, wherein the text matching algorithm is an
algorithm calculates
similarity degree of two texts.
197. The method of claim 196, wherein an unsupervised text matching algorithm,
word mover's
distance (WMD), is used.
198. The method of claim 197, wherein any clustering algorithm is applied to
determine
statement pairs belong to the same statement pair group.
199. The method of claim 198, wherein in all QA pairs, there are invalid QA
pairs caused by
imprecise splitting, and circumstance in which answers are not pertinent to
questions asked
due to negligence of the customer service, wherein invalid QA statement pairs
are
removed.
Date Recue/Date Received 202402-06

200. The method of claim 199, wherein filtration of the QA statement pairs is
decided by
matching degrees of questions and answers, wherein the QA statement pairs
whose
matching degrees between questioning statements and answering statements
satisfy a
preset condition remain, wherein the QA statement pairs whose matching degrees
between
questioning statements and answering statements do not satisfy are eliminated
and filtered
out.
201. The method of claim 200, wherein matching process is a text matching
process, wherein a
set of supervised algorithms are trained based on existing knowledge base data
to perform
similarity calculation.
202. The method of claim 201, wherein frequently asked questions have higher
priorities to be
maintained in the knowledge base.
203. The method of claim 202, wherein more important questions are
preferentially maintained
wherein some less valuable questions are neglected.
204. The method of claim 203, wherein frequencies by which questions are asked
are measured
by the number of questions under each cluster obtained.
205. The method of claim 204, wherein accuracy of answers are measured by
matching degrees
of questions and answers in a filtering process.
206. The method of claim 205, wherein the corresponding sorting weight is
derived by
normalizing two values and weighting and accumulating he two values.
207. The method of claim 206, wherein corresponding statement pairs are
sequentially obtained
according to sorting weights during subsequent maintenance of the knowledge
base, and
screened and processed manually or by machine, and maintained in the knowledge
base.
208. An electronic equipment comprising:
one or more processors;
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Date Recue/Date Received 202402-06

a memory, associated with the one or more processors and used for storing a
program
instruction wherein the program instruction is executed by the one or more
processors
configured to:
obtain a session record, wherein the session record includes at least two
statements, wherein the statements include questioning statements sent by
questioners and answering statements sent by answerers;
split the session record into corresponding groups according to a preset
splitting rule, wherein the groups include at least one questioning statement
and at least one answering statement;
determine a processing rule to which the groups correspond according to a
number of the questioning statements and a number of the answering
statements included in the groups, wherein the processing rule is based on the

number of questioning statements and the number of answering statements as
compared to a first preset threshold;
split the groups into corresponding statement pairs according to the
processing rule to which the groups correspond;
using, when the number of the answering statements included in the group
does not exceed the first preset threshold and the number of the questioning
statements as included exceeds the first preset threshold, a preset binary
classifier to predict whether the questioning statements as included and an
antecedent questioning statements of the questioning statements as included
belong to a same question; and
updating a knowledge base of a system according to the statement pairs.
209. The equipment of claim 208, wherein each statement has a corresponding
generation time.
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210. The equipment of claim 209, wherein splitting the session record into
corresponding
groups according to the preset splitting rule comprises:
sequentially traversing the session record according to generation time of
each
statement;
judging, when the statement traversed is the questioning statement, whether a
traversed
questioning statement and an antecedent questioning statement of the traversed

questioning statement belong to same group according to a sentence pattern of
antecedent answering statement of the traversed questioning statement and/or
according
to an interval time to the antecedent questioning statement of the traversed
questioning
statement; and
determining, when the statement traversed is the answering statement, a
traversed
answering statement belongs to the group to which the antecedent questioning
statement
of the traversed answering statement corresponds.
211. The equipment of claim 210, wherein splitting the groups into the
corresponding statement
pairs according to the processing rule to which the groups correspond
comprises:
splitting, when the number of the questioning statements included in the group
does not
exceed the first preset threshold, the questioning statements each into at
least two text
segments according to preset signs included in the questioning statements;
predicting two adjacent text segments belong to a same question by employing a
preset
binary classifier;
generating corresponding questioning statements respectively according to text

segments predicted to belong to the same question; and
generating the corresponding statement pairs according to all the questioning
statements
as generated and the answering statements included in the group.
212. The equipment of claim 211, further comprises:
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traversing the text segments, and merging traversed text segments with
corresponding
posterior text segments when number of characters of the traversed text
segments is
smaller than a second preset threshold.
213. The equipment of claim 212, further comprises:
merging the traversed text segments with corresponding posterior text segments
by
employing a preset classifier algorithm when the traversed text segments and
the
corresponding posterior text segments belong to a same intent class or when
the
traversed text segments belong to a preset merging intent class.
214. The equipment of claim 213, wherein splitting the groups into the
corresponding statement
pairs according to the processing rule to which the groups correspond
comprises:
combining, when the numbers of the questioning statements and the answering
statements included in the group both exceed the first preset threshold, the
questioning
statements and the answering statements included in the group; and
generating the corresponding statement pairs.
215. The equipment of claim 214, wherein splitting the groups into the
corresponding statement
pairs according to the processing rule to which the groups correspond
comprises:
merging, when there are the questioning statements belong to the same
question, the
questioning statements belong to the same question and generating the
corresponding
statement pairs according to all merged questioning statements and the
answering
statements; and
generating the corresponding statement pairs according to all the questioning
statements
and the answering statements included in the group, when there are no
questioning
statements belong to the same question.
216. The equipment of claim 215, wherein updating the knowledge base of the
system
according to the statement pairs comprises:
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clustering the statement pairs by using a preset clustering algorithm;
generating statement pair groups;
determining the number of the questioning statements included in each
statement pair
group;
determining matching degrees between the questioning statements and the
answering
statements included in the statement pair groups according to a preset
similarity
algorithm;
determining a weight to each statement pair group corresponds according to
corresponding matching degrees and number of questioning statements included
in the
statement pair groups; and
sequentially150 updating the knowledge base of the system according to the
weight to
which each statement pair group corresponds.
217. The equipment of claim 216, further comprises:
rectifying any wrong word included in the session record according to a preset

rectifying rule; and
performing a normalizing process on rectified session record.
218. The equipment of claim 217, further comprises:
recognizing the intent class to which each questioning statement included in
the session
record corresponds by using the preset classifier algorithm and eliminating
any
questioning statement to which a preset irrelevant intent class corresponds as
included
in the session record.
219. The equipment of claim 218, further comprises a process of analyzing and
mining dialogue
statements between customer service and a user comprising:
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obtaining a session record to be processed and preprocessing obtained session
record;
and
rectifying any wrong word included in the session record according to a preset

rectifying rule.
220. The equipment of claim 219, further comprises:
perfoiming a purification operation on all characters included in the session
record; and
recognizing a dialogue intent to which the questioning statement sent by each
user
corresponds by using the preset classifier algorithm.
221. The equipment of claim 220, further comprises:
traversing a dialogue according to a temporal sequence of generation times;
screening questioning statements to be processed of the user when the
questioning
statements to be processed are traversed, and eliminating any questioning
statement to
be processed of the user does not conform to a preset condition;
determining a questioning statement to be processed is to be merged with the
antecedent
questioning statement of the questioning statement to be processed according
to a preset
merging rule;
eliminating any answering statement whose number of characters is smaller than
a
preset number threshold when the answering statements of the customer service
are
traversed, and determining any answering statement remaining after the
elimination as
an answering statement to be processed;
merging the answering statement to be processed with an antecedent answering
statement when the antecedent statement of the answering statement to be
processed is
an answering statement of the customer service, and storing the merged
answering
statement to the group to which the antecedent questioning statement
corresponds;
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merging the answering statement to be processed in the group to which the
questioning
statement corresponds when the antecedent statement of the answering statement
to be
processed is a questioning statement of the user;
222. The equipment of claim 221, further comprises
splitting the questioning statements included in the group each into text
segments when
the group is of a QA type;
processing the text segments from front to back, and merging any text segment
whose
number of characters is smaller than the preset number threshold in a
posterior text
segment of this text segment; and/or
merging any text segment pertaining to the same intent class as the
corresponding
posterior text segment or pertaining to a preset merging intent class in the
posterior text
segment of this text segment;
sequentially obtaining a preset number of adjacent text segments through a
sliding
window, and predicting obtained text segments belong to a same and single
question by
means of a binary classifier algorithm;
traversing the questioning statements included in the group and judging each
questioning statement and its antecedent questioning statement belong to the
same
question when the group is of a QQA type; and
combining all questioning statements and answering statements in pairs to
generate
corresponding OA statement pairs when the group is of a QAQA type.
223. The equipment of claim 222 further comprises:
clustering the statements by employing a preset clustering algorithm,
generating
statement pair groups, and determining the number of questioning statements
included
in each statement pair group;
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determining matching degrees between the questioning statements and the
answering
statements included in the statement pair groups according to a preset
similarity
algorithm; and
determining a weight to which each statement pair group corresponds according
to the
corresponding matching degrees and the numbers of questioning statements
included in
the statement pair groups.
224. The equipment of claim 223, wherein the session record is directed to
text statements,
principal wrong words are homonyms.
225. The equipment of claim 224, wherein the session record is directed to a
speech statements,
the session record is firstly required to convert the speech statements into
the text
statements through speech recognition technique.
226. The equipment of claim 225, wherein language model and word frequency
features are
combined.
227. The equipment of claim 226, wherein corresponding rectifying rules are
provided for the
speech statements and the text statements respectively.
228. The equipment of claim 227, wherein a wrong words are rectified according
to the
corresponding rectifying rules.
229. The equipment of claim 228, wherein the purification operation includes
removing
irrelevant characters including preset useless punctuations and preset stop
words,
recognizing irrelevant infoimation contained in each text statement including
commodity
names and placenames and normalizing the irrelevant information to
corresponding preset
characters according to which the irrelevant information corresponds.
230. The equipment of claim 229, the session record of the user with customer
service within
one day is a segment of the dialogue.
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231. The equipment of claim 230, wherein the session record is split into one
or more
dialogues, and the dialogues are split into groups.
232. The equipment of claim 231, wherein the user consults same type of
questions within a
preset period of time, wherein the customer service has replied, the user
consults different
questions next time in the dialogue with the customer service.
233. The equipment of claim 232, wherein to eliminate any questioning
statement with
eliminable intent and irrelevant to business whose number of characters is
smaller than a
preset number threshold or intent is judged by the preset classifier algorithm
as chitchat
intent.
234. The equipment of claim 233, wherein the interval time between the
antecedent questioning
statement of the questioning statement to be processed and the questioning
statement to be
processed exceeds a corresponding preset time threshold and/or when the
sentence pattern
of the antecedent answering statement of the questioning statement to be
processed is a
preset sentence pattern, the questioning statement to be processed is merged
with its
antecedent questioning statement.
235. The equipment of claim 234, wherein the antecedent questioning statement
is a
questioning statement that is temporally antecedent to the statement to be
processed and
with a shortest interval time to the statement to be processed.
236. The equipment of claim 235, wherein the antecedent answering statement is
an answering
statement that is temporally antecedent to the statement to be processed and
with the
shortest interval time to the statement to be processed.
237. The equipment of claim 236, wherein the interval time between the
antecedent questioning
statement of the questioning statement to be processed and the questioning
statement to be
processed exceeds the corresponding preset time threshold, judge there is no
relevancy
between the antecedent questioning statement and the questioning statement to
be
processed, so a new group is generated according to the questioning statement
to be
processed.
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238. The equipment of claim 237, wherein the corresponding preset time
threshold is not
exceeded, judge there is relevancy between the antecedent questioning
statement and the
questioning statement to be processed, and the questioning statement to be
processed is
merged in the group to which the antecedent questioning statement corresponds.
239. The equipment of claim 238, wherein the preset sentence patterns include
statements that
guide the user to further respond to responses made by the customer service,
includes asks
in reply by the customer service to indefinite expressions of users, or the
sentence pattern
asking for essential information from the user.
240. The equipment of claim 239, wherein the antecedent answering statement is
of the preset
sentence pattern, the statement sent by the user after the antecedent
answering statement is
made in reply to this antecedent answering statement and is relevant to the
antecedent
answering statement, and the questioning statement to be processed is merged
with the
antecedent questioning statement.
241. The equipment of claim 240, wherein the antecedent answering statement of
the
questioning statement to be processed is of the preset sentence pattern, the
questioning
statement to be processed is merged with the antecedent questioning statement,
and the
questioning statement to be processed is merged in the group to which the
antecedent
questioning statement corresponds.
242. The equipment of claim 241, wherein the antecedent statement is a
statement that is
temporally antecedent to the statement to be processed and with the shortest
interval time
to the statement to be processed.
243. The equipment of claim 242, wherein splitting the dialogue into groups,
wherein result
includes three types of groups comprising:
one question corresponds to a segment of reply, is marked as QA;
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plural questions correspond to a segment of reply, wherein the user asks
plural
questions and the customer service replies with a segment of words, is marked
as QQA;
and
plural questions correspond to plural replies, wherein several rounds of
communication
are carried out between the user and the customer service in a short time, is
marked as
QAQA.
244. The equipment of claim 243, wherein corresponding type is determined
according to
number of answering statements and questioning statements included in each
group, and is
processed according to corresponding processing rule.
245. The equipment of claim 244, wherein QA is a standard input foiiii of a
algorithm, wherein
one standard question is only meant to express one question in the knowledge
base.
246. The equipment of claim 245, wherein an auxiliary algorithm is required
during splitting to
judge whether two segments of words are directed to one question or to two
questions.
247. The equipment of claim 246, wherein the auxiliary algorithm is a binary
classifier, wherein
inputs to the binary classifier are two statements.
248. The equipment of claim 247, wherein any model realizes binary questions.
249. The equipment of claim 248, wherein model bert predicts during the
process of pretraining
whether input two statements are directed to context of the same and single
statement or
topics irrelevant to each other, serves as the classifier, and fine-tuning
training is
perfoimed.
250. The equipment of claim 249, wherein the posterior text segment indicates
a text segment
following and immediately adjacent to the text segment being processed.
251. The equipment of claim 250, wherein the classifier algorithm merges any
text segment
judged as pertaining to the same intent class as the posterior text segment or
pertaining to
the preset merging intent class as a chitchat class in the posterior text
segment.
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252. The equipment of claim 251, wherein text segments predicted to belong to
the same
question is merged into one questioning statement.
253. The equipment of claim 252, wherein the text segments predicted to belong
to different
questions are split into two different questioning statements.
254. The equipment of claim 253, wherein the groups of the QA type not
belonging to the same
and single question are converted to groups of the QQA type, wherein the
groups of the
QA type whose all text segments belong to the same and single question are
split into a
QA statement pair only includes one questioning statement and one answering
statement.
255. The equipment of claim 254, wherein to recognize to which circumstance a
group of the
QQA type specifically pertains, judge through a binary classification
algorithm.
256. The equipment of claim 255, wherein the questioning statement is split
into text segments,
and any text segment whose number of characters included is smaller than the
preset
number threshold or pertaining to the preset merging intent is directly merged
with the
antecedent questioning statement, or the questioning statement and the
antecedent
questioning statement are input together in the binary classification
algorithm to judge they
belong to the same question.
257. The equipment of claim 256, wherein it is judged any text segment and
corresponding
antecedent questioning statement belong to the same question, the text
statement and the
corresponding antecedent questioning statement are merged into one questioning

statement.
258. The equipment of claim 257, wherein it is recognized any text segment and
the
corresponding antecedent questioning statement do not belong to the same
question, the
questioning statement is split into new questioning statements.
259. The equipment of claim 258, wherein the statements remain are only one
answering
statement and one questioning statement, they are determined as the QA
statement pair.
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260. The equipment of claim 259, wherein the statements remain are more than
one questioning
statement and one answering statement, the questioning statements and the
answering
statements are combined in pairs to generate corresponding QA statement pairs.
261. The equipment of claim 260, wherein interaction between the user and the
customer
service in the short time is split into plural groups of QA statement pairs.
262. The equipment of claim 261, wherein the answering statements and the
questioning
statements are directly combined in pairs with respect to groups of the QAQA
type.
263. The equipment of claim 262, wherein clustering is to incorporate similar
questions
together to constitute a cluster.
264. The equipment of claim 263, wherein calculate text distance metrics
amongst the
statement pairs via a text matching algorithm and determine the statement
pairs belong to
same statement pair group according to the text distance metrics.
265. The equipment of claim 264, wherein the text matching algorithm is an
algorithm
calculates similarity degree of two texts.
266. The equipment of claim 265, wherein an unsupervised text matching
algorithm, word
mover's distance (WMD), is used.
267. The equipment of claim 266, wherein any clustering algorithm is applied
to determine
statement pairs belong to the same statement pair group.
268. The equipment of claim 267, wherein in all QA pairs, there are invalid QA
pairs caused by
imprecise splitting, and circumstance in which answers are not pertinent to
questions asked
due to negligence of the customer service, wherein invalid QA statement pairs
are
removed.
269. The equipment of claim 268, wherein filtration of the QA statement pairs
is decided by
matching degrees of questions and answers, wherein the QA statement pairs
whose
matching degrees between questioning statements and answering statements
satisfy a
preset condition remain, wherein the QA statement pairs whose matching degrees
between
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questioning statements and answering statements do not satisfy are eliminated
and filtered
out.
270. The equipment of claim 269, wherein matching process is a text matching
process,
wherein a set of supervised algorithms are trained based on existing knowledge
base data
to perform similarity calculation.
271. The equipment of claim 270, wherein frequently asked questions have
higher priorities to
be maintained in the knowledge base.
272. The equipment of claim 271, wherein more important questions are
preferentially
maintained wherein some less valuable questions are neglected.
273. The equipment of claim 272, wherein frequencies by which questions are
asked are
measured by the number of questions under each cluster obtained.
274. The equipment of claim 273, wherein accuracy of answers are measured by
matching
degrees of questions and answers in a filtering process.
275. The equipment of claim 274, wherein the corresponding sorting weight is
derived by
normalizing two values and weighting and accumulating he two values.
276. The equipment of claim 275, wherein corresponding statement pairs are
sequentially
obtained according to sorting weights during subsequent maintenance of the
knowledge
base, and screened and processed manually or by machine, and maintained in the

knowledge base.
277.A computer readable physical memory having stored thereon, computer-
executable
instructions, when executed by a computer, the computer is configured to:
obtain a session record, wherein the session record includes at least two
statements,
wherein the statements include questioning statements sent by questioners and
answering statements sent by answerers;
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split the session record into corresponding groups according to a preset
splitting rule,
wherein the groups include at least one questioning statement and at least one

answering statement;
deteimine a processing rule to which the groups correspond according to a
number of
the questioning statements and a number of the answering statements included
in the
groups, wherein the processing rule is based on the number of questioning
statements
and the number of answering statements as compared to a first preset
threshold;
split the groups into corresponding statement pairs according to the
processing rule to
which the groups correspond;
using, when the number of the answering statements included in the group does
not
exceed the first preset threshold and the number of the questioning statements
as
included exceeds the first preset threshold, a preset binary classifier to
predict whether
the questioning statements as included and an antecedent questioning
statements of the
questioning statements as included belong to a same question; and
updating a knowledge base of a system according to the statement pairs.
278. The memory of claim 277, wherein each statement has a corresponding
generation time.
279. The memory of claim 278, wherein splitting the session record into
corresponding groups
according to the preset splitting rule comprises:
sequentially traversing the session record according to generation time of
each
statement;
judging, when the statement traversed is the questioning statement, whether a
traversed
questioning statement and an antecedent questioning statement of the traversed

questioning statement belong to same group according to a sentence pattern of
antecedent answering statement of the traversed questioning statement and/or
according
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to an interval time to the antecedent questioning statement of the traversed
questioning
statement; and
determining, when the statement traversed is the answering statement, a
traversed
answering statement belongs to the group to which the antecedent questioning
statement
of the traversed answering statement corresponds.
280. The memory of claim 279, wherein splitting the groups into the
corresponding statement
pairs according to the processing rule to which the groups correspond
comprises:
splitting, when the number of the questioning statements included in the group
does not
exceed the first preset threshold, the questioning statements each into at
least two text
segments according to preset signs included in the questioning statements;
predicting two adjacent text segments belong to a same question by employing a
preset
binary classifier;
generating corresponding questioning statements respectively according to text

segments predicted to belong to the same question; and
generating the corresponding statement pairs according to all the questioning
statements
as generated and the answering statements included in the group.
281. The memory of claim 280, further comprises:
traversing the text segments, and merging traversed text segments with
corresponding
posterior text segments when number of characters of the traversed text
segments is
smaller than a second preset threshold;
282. The memory of claim 281, further comprises:
merging the traversed text segments with corresponding posterior text segments
by
employing a preset classifier algorithm when the traversed text segments and
the
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corresponding posterior text segments belong to a same intent class or when
the
traversed text segments belong to a preset merging intent class.
283. The memory of claim 282, wherein splitting the groups into the
corresponding statement
pairs according to the processing rule to which the groups correspond
comprises:
combining, when the numbers of the questioning statements and the answering
statements included in the group both exceed the first preset threshold, the
questioning
statements and the answering statements included in the group; and
generating the corresponding statement pairs.
284. The memory of claim 283, wherein splitting the groups into the
corresponding statement
pairs according to the processing rule to which the groups correspond
comprises:
merging, when there are the questioning statements belong to the same
question, the
questioning statements belong to the same question and generating the
corresponding
statement pairs according to all merged questioning statements and the
answering
statements; and
generating the corresponding statement pairs according to all the questioning
statements
and the answering statements included in the group, when there are no
questioning
statements belong to the same question.
285. The memory of claim 284, wherein updating the knowledge base of the
system according
to the statement pairs comprises:
clustering the statement pairs by using a preset clustering algorithm;
generating statement pair groups;
determining the number of the questioning statements included in each
statement pair
group;
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determining matching degrees between the questioning statements and the
answering
statements included in the statement pair groups according to a preset
similarity
algorithm;
determining a weight to each statement pair group corresponds according to
corresponding matching degrees and number of questioning statements included
in the
statement pair groups; and
sequentially150 updating the knowledge base of the system according to the
weight to
which each statement pair group corresponds.
286. The memory of claim 285, further comprises:
rectifying any wrong word included in the session record according to a preset

rectifying rule; and
performing a normalizing process on rectified session record.
287. The memory of claim 286, further comprises:
recognizing the intent class to which each questioning statement included in
the session
record corresponds by using the preset classifier algorithm and eliminating
any
questioning statement to which a preset irrelevant intent class corresponds as
included
in the session record.
288. The memory of claim 287, further comprises a process of analyzing and
mining dialogue
statements between customer service and a user comprising:
obtaining a session record to be processed and preprocessing obtained session
record;
and
rectifying any wrong word included in the session record according to a preset

rectifying rule.
289. The memory of claim 288, further comprises:
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performing a purification operation on all characters included in the session
record; and
recognizing a dialogue intent to which the questioning statement sent by each
user
corresponds by using the preset classifier algorithm.
290. The memory of claim 289, further comprises:
traversing a dialogue according to a temporal sequence of generation times;
screening questioning statements to be processed of the user when the
questioning
statements to be processed are traversed, and eliminating any questioning
statement to
be processed of the user does not conform to a preset condition;
determining a questioning statement to be processed is to be merged with the
antecedent
questioning statement of the questioning statement to be processed according
to a preset
merging rule;
eliminating any answering statement whose number of characters is smaller than
a
preset number threshold when the answering statements of the customer service
are
traversed, and determining any answering statement remaining after the
elimination as
an answering statement to be processed;
merging the answering statement to be processed with an antecedent answering
statement when the antecedent statement of the answering statement to be
processed is
an answering statement of the customer service, and storing the merged
answering
statement to the group to which the antecedent questioning statement
corresponds; and
merging the answering statement to be processed in the group to which the
questioning
statement corresponds when the antecedent statement of the answering statement
to be
processed is a questioning statement of the user.
291. The memory of claim 290, further comprises
splitting the questioning statements included in the group each into text
segments when
the group is of a QA type;
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processing the text segments from front to back, and merging any text segment
whose
number of characters is smaller than the preset number threshold in a
posterior text
segment of this text segment; and/or
merging any text segment pertaining to the same intent class as the
corresponding
posterior text segment or pertaining to a preset merging intent class in the
posterior text
segment of this text segment;
sequentially obtaining a preset number of adjacent text segments through a
sliding
window, and predicting obtained text segments belong to a same and single
question by
means of a binary classifier algorithm;
traversing the questioning statements included in the group and judging each
questioning statement and its antecedent questioning statement belong to the
same
question when the group is of a QQA type; and
combining all questioning statements and answering statements in pairs to
generate
corresponding OA statement pairs when the group is of a QAQA type.
292. The memory of claim 291 further comprises:
clustering the statements by employing a preset clustering algorithm,
generating
statement pair groups, and determining the number of questioning statements
included
in each statement pair group;
determining matching degrees between the questioning statements and the
answering
statements included in the statement pair groups according to a preset
similarity
algorithm; and
determining a weight to which each statement pair group corresponds according
to the
corresponding matching degrees and the numbers of questioning statements
included in
the statement pair groups.
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293. The memory of claim 292, wherein the session record is directed to text
statements,
principal wrong words are homonyms.
294. The memory of claim 293, wherein the session record is directed to a
speech statements,
the session record is firstly required to convert the speech statements into
the text
statements through speech recognition technique.
295. The memory of claim 294, wherein language model and word frequency
features are
combined.
296. The memory of claim 295, wherein corresponding rectifying rules are
provided for the
speech statements and the text statements respectively.
297. The memory of claim 296, wherein a wrong words are rectified according to
the
corresponding rectifying rules.
298. The memory of claim 297, wherein the purification operation includes
removing irrelevant
characters including preset useless punctuations and preset stop words,
recognizing
irrelevant information contained in each text statement including commodity
names and
placenames and normalizing the irrelevant information to corresponding preset
characters
according to which the irrelevant information corresponds.
299. The memory of claim 298, the session record of the user with customer
service within one
day is a segment of the dialogue.
300. The memory of claim 299, wherein the session record is split into one or
more dialogues,
and the dialogues are split into groups.
301. The memory of claim 300, wherein the user consults same type of questions
within a
preset period of time, wherein the customer service has replied, the user
consults different
questions next time in the dialogue with the customer service.
302. The memory of claim 301, wherein to eliminate any questioning statement
with eliminable
intent and irrelevant to business whose number of characters is smaller than a
preset
number threshold or intent is judged by the preset classifier algorithm as
chitchat intent.
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303. The memory of claim 302, wherein the interval time between the antecedent
questioning
statement of the questioning statement to be processed and the questioning
statement to be
processed exceeds a corresponding preset time threshold and/or when the
sentence pattern
of the antecedent answering statement of the questioning statement to be
processed is a
preset sentence pattern, the questioning statement to be processed is merged
with its
antecedent questioning statement.
304. The memory of claim 303, wherein the antecedent questioning statement is
a questioning
statement that is temporally antecedent to the statement to be processed and
with a shortest
interval time to the statement to be processed.
305. The memory of claim 304, wherein the antecedent answering statement is an
answering
statement that is temporally antecedent to the statement to be processed and
with the
shortest interval time to the statement to be processed.
306. The memory of claim 305, wherein the interval time between the antecedent
questioning
statement of the questioning statement to be processed and the questioning
statement to be
processed exceeds the corresponding preset time threshold, judge there is no
relevancy
between the antecedent questioning statement and the questioning statement to
be
processed, so a new group is generated according to the questioning statement
to be
processed.
307. The memory of claim 306, wherein the corresponding preset time threshold
is not
exceeded, judge there is relevancy between the antecedent questioning
statement and the
questioning statement to be processed, and the questioning statement to be
processed is
merged in the group to which the antecedent questioning statement corresponds.
308. The memory of claim 307, wherein the preset sentence patterns include
statements that
guide the user to further respond to responses made by the customer service,
includes asks
in reply by the customer service to indefinite expressions of users, or the
sentence pattern
asking for essential information from the user.
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309. The memory of claim 308, wherein the antecedent answering statement is of
the preset
sentence pattern, the statement sent by the user after the antecedent
answering statement is
made in reply to this antecedent answering statement and is relevant to the
antecedent
answering statement, and the questioning statement to be processed is merged
with the
antecedent questioning statement.
310. The memory of claim 309, wherein the antecedent answering statement of
the questioning
statement to be processed is of the preset sentence pattern, the questioning
statement to be
processed is merged with the antecedent questioning statement, and the
questioning
statement to be processed is merged in the group to which the antecedent
questioning
statement corresponds.
311. The memory of claim 310, wherein the antecedent statement is a statement
that is
temporally antecedent to the statement to be processed and with the shortest
interval time
to the statement to be processed.
312. The memory of claim 311, wherein splitting the dialogue into groups,
wherein result
includes three types of groups comprising:
one question corresponds to a segment of reply, is marked as QA;
plural questions correspond to a segment of reply, wherein the user asks
plural
questions and the customer service replies with a segment of words, is marked
as QQA;
and
plural questions correspond to plural replies, wherein several rounds of
communication
are carried out between the user and the customer service in a short time, is
marked as
QAQA.
313. The memory of claim 312, wherein corresponding type is determined
according to
number of answering statements and questioning statements included in each
group, and is
processed according to corresponding processing rule.
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314. The memory of claim 313, wherein QA is a standard input form of a
algorithm, wherein
one standard question is only meant to express one question in the knowledge
base.
315. The memory of claim 314, wherein an auxiliary algorithm is required
during splitting to
judge whether two segments of words are directed to one question or to two
questions.
316. The memory of claim 315, wherein the auxiliary algorithm is a binary
classifier, wherein
inputs to the binary classifier are two statements.
317. The memory of claim 316, wherein any model realizes binary questions.
318. The memory of claim 317, wherein model bert predicts during the process
of pretraining
whether input two statements are directed to context of the same and single
statement or
topics irrelevant to each other, serves as the classifier, and fine-tuning
training is
performed.
319. The memory of claim 318, wherein the posterior text segment indicates a
text segment
following and immediately adjacent to the text segment being processed.
320. The memory of claim 319, wherein the classifier algorithm merges any text
segment
judged as pertaining to the sarne intent class as the posterior text segment
or pertaining to
the preset merging intent class as a chitchat class in the posterior text
segment.
321. The memory of claim 320, wherein text segments predicted to belong to the
same question
is merged into one questioning statement.
322. The memory of claim 321, wherein the text segments predicted to belong to
different
questions are split into two different questioning statements.
323. The memory of claim 322, wherein the groups of the QA type not belonging
to the same
and single question are converted to groups of the QQA type, wherein the
groups of the
QA type whose all text segments belong to the same and single question are
split into a
QA statement pair only includes one questioning statement and one answering
statement.
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324. The memory of claim 323, wherein to recognize to which circumstance a
group of the
QQA type specifically pertains, judge through a binary classification
algorithm.
325. The memory of claim 324, wherein the questioning statement is split into
text segments,
and any text segment whose number of characters included is smaller than the
preset
number threshold or pertaining to the preset merging intent is directly merged
with the
antecedent questioning statement, or the questioning statement and the
antecedent
questioning statement are input together in the binary classification
algorithm to judge they
belong to the same question.
326. The memory of claim 325, wherein it is judged any text segment and
corresponding
antecedent questioning statement belong to the same question, the text
statement and the
corresponding antecedent questioning statement are merged into one questioning

statement.
327. The memory of claim 326, wherein it is recognized any text segment and
the
corresponding antecedent questioning statement do not belong to the same
question, the
questioning statement is split into new questioning statements.
328. The memory of claim 327, wherein the statements remain are only one
answering
statement and one questioning statement, they are determined as the QA
statement pair.
329. The memory of claim 328, wherein the statements remain are more than one
questioning
statement and one answering statement, the questioning statements and the
answering
statements are combined in pairs to generate corresponding QA statement pairs.
330. The memory of claim 329, wherein interaction between the user and the
customer service
in the short time is split into plural groups of QA statement pairs.
331. The memory of claim 330, wherein the answering statements and the
questioning
statements are directly combined in pairs with respect to groups of the QAQA
type.
332. The memory of claim 331, wherein clustering is to incorporate similar
questions together
to constitute a cluster.
Date Recue/Date Received 202402-06

333. The memory of claim 332, wherein calculate text distance metrics amongst
the statement
pairs via a text matching algorithm and determine the statement pairs belong
to same
statement pair group according to the text distance metrics.
334. The memory of claim 333, wherein the text matching algorithm is an
algorithm calculates
similarity degree of two texts.
335. The memory of claim 334, wherein an unsupervised text matching algorithm,
word
mover's distance (WMD), is used.
336. The memory of claim 335, wherein any clustering algorithm is applied to
deterinine
statement pairs belong to the same statement pair group.
337. The memory of claim 336, wherein in all QA pairs, there are invalid QA
pairs caused by
imprecise splitting, and circumstance in which answers are not pertinent to
questions asked
due to negligence of the customer service, wherein invalid QA statement pairs
are
removed.
338. The memory of claim 337, wherein filtration of the QA statement pairs is
decided by
matching degrees of questions and answers, wherein the QA statement pairs
whose
matching degrees between questioning statements and answering statements
satisfy a
preset condition remain, wherein the QA statement pairs whose matching degrees
between
questioning statements and answering statements do not satisfy are eliminated
and filtered
out.
339. The memory of claim 338, wherein matching process is a text matching
process, wherein a
set of supervised algorithms are trained based on existing knowledge base data
to perform
similarity calculation.
340. The memory of claim 339, wherein frequently asked questions have higher
priorities to be
maintained in the knowledge base.
341. The memory of claim 340, wherein more important questions are
preferentially maintained
wherein some less valuable questions are neglected.
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342. The memory of claim 341, wherein frequencies by which questions are asked
are
measured by the number of questions under each cluster obtained.
343. The memory of claim 342, wherein accuracy of answers are measured by
matching
degrees of questions and answers in a filtering process.
344. The memory of claim 343, wherein the corresponding sorting weight is
derived by
normalizing two values and weighting and accumulating he two values.
345. The memory of claim 344, wherein corresponding statement pairs are
sequentially
obtained accorcfing to sorting weights during subsequent maintenance of the
knowledge
base, and screened and processed manually or by machine, and maintained in the

knowledge base.
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Description

A PROCESSING METHOD, DEVICE AND ELECTRONIC DEVICE FORA
QUESTION-AND-ANSWER STATEMENT
BACKGROUND OF THE INVENTION
Technical Field
[0001] The present invention relates to the field of natural language
processing technology,
and more particularly to a Q&A statement processing method, and corresponding
device and electronic equipment.
Description of Related Art
[0002] In the traditional service industry, as a labor-intensive post, human
customer service is
a highly intensive and highly repetitive job over the entire time period.
Accordingly, in
order to reduce manpower cost and enhance efficiency, more and more
enterprises have
introduced the automatic Q&A system enabling automatic response with
corresponding
answering statements to questions raised by users, alleviating the pressure of
human
customer service to a certain degree, and enhancing accuracy, standardization,
and
stability of enterprise services.
[0003] However, in order to guarantee that the automatic Q&A system can
accurately respond
to users, it is needed to maintain a colossal knowledge base system therefor.
The
knowledge base contains great quantities of standard questions and
corresponding
answers, while the Q&A process of the intelligent customer service of the Q&A
system
is mainly employed to match questions of users with standard questions in the
knowledge base, if matching succeeds, answers corresponding to the standard
questions
are returned. Accordingly, the comprehensive degree of the knowledge base is
the
deciding factor affecting the response effect of the intelligent customer
service system.
However, users' questions have never been immutable, and it is frequent for
users to
raise new questions not subsumed in the knowledge base due to diversified
reasons, so
it is of absolute necessity to maintain and update the knowledge base. In
addition, the
traditional role played by humans would not disappear in such Q&A systems as
intelligent customer service, as humans would usually make certain
supplementary
corrections on questions unanswerable by or wrongly answered by intelligent
customer
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Date Regue/Date Received 2022-06-29

service.
[0004] Therefore, there is an urgent need to propose a Q&A statement
processing method, and
corresponding device and electronic equipment capable of analyzing and mining
human
session data to generate Q&A pairs highly effectively, so as to solve the
above technical
problems pending in the state of the art.
SUMMARY OF THE INVENTION
[0005] In order to address deficiencies prevailing in prior-art technology, an
main objective of
the present invention is to provide a Q&A statement processing method, and
corresponding device and electronic equipment, so as to solve the technical
problems
in the state of the art.
[0006] To achieve the above objective, according to one aspect, the present
invention provides
a Q&A statement processing method that comprises:
[0007] obtaining a session record to be processed, wherein the session record
includes at least
two statements, and the statements include questioning statements sent by
questioners
and answering statements sent by answerers;
[0008] splitting the session record into corresponding Q&A groups according to
a preset Q&A
splitting rule, wherein the Q&A groups include at least one questioning
statement and
at least one answering statement;
[0009] determining a processing rule to which the Q&A groups correspond
according to the
number of the questioning statement(s) and the number of the answering
statement(s)
included in the Q&A groups;
[0010] splitting the Q&A groups into corresponding statement pairs according
to the
processing rule to which the Q&A groups correspond; and
[0011] updating a knowledge base of a Q&A system according to the statement
pairs.
[0012] In some embodiments, each statement has a corresponding generation
time, and the
step of splitting the session record into corresponding Q&A groups according
to a preset
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Q&A splitting rule includes:
[0013] sequentially traversing the session record according to the generation
time of each
statement;
[0014] judging, when the statement traversed is a questioning statement,
whether the traversed
questioning statement and the antecedent questioning statement of the
traversed
questioning statement belong to the same Q&A group according to a sentence
pattern
of the antecedent answering statement of the traversed questioning statement
and/or
according to an interval time to the antecedent questioning statement of the
traversed
questioning statement; and
[0015] determining, when the statement traversed is an answering statement,
that the traversed
answering statement belongs to the Q&A group to which the antecedent
questioning
statement of the traversed answering statement corresponds.
[0016] In some embodiments, the step of splitting the Q&A groups into
corresponding
statement pairs according to the processing rule to which the Q&A groups
correspond
includes:
[0017] splitting, when the number of the questioning statements included in
the Q&A group
does not exceed a first preset threshold, the questioning statements each into
at least
two text segments according to preset signs included in the questioning
statements;
[0018] predicting whether two adjacent text segments belong to the same
question by
employing a preset binary classifier;
[0019] generating corresponding questioning statements respectively according
to text
segments predicted to belong to the same question; and
[0020] generating corresponding statement pairs according to all the
questioning statements as
generated and the answering statements included in the Q&A group.
[0021] In some embodiments, before the step of predicting whether two adjacent
text segments
belong to the same question by employing a preset binary classifier, the
method further
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comprises:
[0022] traversing the text segments, and merging the traversed text segments
with
corresponding posterior text segments when the number of characters of the
traversed
text segments is smaller than a second preset threshold; and/or
[0023] merging the traversed text segments with corresponding posterior text
segments by
employing a preset classifier algorithm when the traversed text segments and
the
corresponding posterior text segments belong to the same intent class or when
the
traversed text segments belong to a preset merging intent class.
[0024] In some embodiments, the step of splitting the Q&A groups into
corresponding
statement pairs according to the processing rule to which the Q&A groups
correspond
includes:
[0025] combining, when the numbers of the questioning statements and the
answering
statements included in the Q&A group both exceed the first preset threshold,
the
questioning statements and the answering statements included in the Q&A group,
and
generating the corresponding statement pairs.
[0026] In some embodiments, the step of splitting the Q&A groups into
corresponding
statement pairs according to the processing rule to which the Q&A groups
correspond
includes:
[0027] employing, when the number of the answering statements included in the
Q&A group
does not exceed the first preset threshold and the number of the questioning
statements
as included exceeds the first preset threshold, the preset binary classifier
to predict
whether the questioning statements as included and the antecedent questioning
statements of the questioning statements as included belong to the same
question;
[0028] merging, when there are the questioning statements that belong to the
same question,
the questioning statements that belong to the same question and generating the

corresponding statement pairs according to all the merged questioning
statements and
the answering statements; and
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[0029] generating the corresponding statement pairs according to all the
questioning
statements and the answering statements included in the Q&A group, when there
are no
questioning statements that belong to the same question.
[0030] In some embodiments, the step of updating a knowledge base of a Q&A
system
according to the statement pairs includes:
[0031] clustering the statement pairs by employing a preset clustering
algorithm, generating
statement pair groups, and determining the number of the questioning
statements
included in each statement pair group;
[0032] determining matching degrees between the questioning statements and the
answering
statements included in the statement pair groups according to a preset
similarity
algorithm;
[0033] determining a weight to which each statement pair group corresponds
according to the
corresponding matching degrees and the numbers of questioning statements
included
in the statement pair groups; and
[0034] sequentially updating the knowledge base of the Q&A system according to
the weight
to which each statement pair group corresponds.
[0035] In some embodiments, before the step of splitting the session record
into corresponding
Q&A groups according to a preset Q&A splitting rule, the method further
comprises:
[0036] rectifying any wrong word included in the session record according to a
preset
rectifying rule; and
[0037] performing a normalizing process on the rectified session record.
[0038] In some embodiments, before the step of splitting the session record
into corresponding
Q&A groups according to a preset Q&A splitting rule, the method further
comprises:
[0039] recognizing the intent class to which each questioning statement
included in the session
record corresponds by employing the preset classifier algorithm and
eliminating any
questioning statement to which a preset irrelevant intent class corresponds as
included
Date Regue/Date Received 2022-06-29

in the session record.
[0040] According to the second aspect, the present application provides a Q&A
statement
processing device that comprises:
[0041] an obtaining module, for obtaining a session record to be processed,
wherein the session
record includes at least two statements, and the statements include
questioning
statements sent by questioners and answering statements sent by answerers;
[0042] a splitting module, for splitting the session record into corresponding
Q&A groups
according to a preset Q&A splitting rule, wherein the Q&A groups include at
least one
questioning statement and at least one answering statement;
[0043] a judging module, for determining a processing rule to which the Q&A
groups
correspond according to the number of the questioning statement(s) and the
number of
the answering statement(s) included in the Q&A groups; wherein
[0044] the splitting module is further employed for splitting the Q&A groups
into
corresponding statement pairs according to the processing rule to which the
Q&A
groups correspond; and
[0045] an updating module, for updating a knowledge base of a Q&A system
according to the
statement pairs.
[0046] According to the third aspect, the present application provides an
electronic equipment
that comprises:
[0047] one or more processor(s); and
[0048] a memory, associated with the one or more processor(s) and used for
storing a program
instruction. The program instruction executes the following operations when it
is read
and executed by the one or more processor(s):
[0049] obtaining a session record to be processed, wherein the session record
includes at least
two statements, and the statements include questioning statements sent by
questioners
and answering statements sent by answerers;
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[0050] splitting the session record into corresponding Q&A groups according to
a preset Q&A
splitting rule, wherein the Q&A groups include at least one questioning
statement and
at least one answering statement;
[0051] determining a processing rule to which the Q&A groups correspond
according to the
number of the questioning statement(s) and the number of the answering
statement(s)
included in the Q&A groups;
[0052] splitting the Q&A groups into corresponding statement pairs according
to the
processing rule to which the Q&A groups correspond; and
[0053] updating a knowledge base of a Q&A system according to the statement
pairs.
[0054] The present invention achieves the following advantageous effects.
[0055] The present application provides a Q&A statement processing method,
comprising
splitting the session record into corresponding Q&A groups according to a
preset Q&A
splitting rule, wherein the Q&A groups include at least one questioning
statement and
at least one answering statement; determining a processing rule to which the
Q&A
groups correspond according to the number of the questioning statement(s) and
the
number of the answering statement(s) included in the Q&A groups; splitting the
Q&A
groups into corresponding statement pairs according to the processing rule to
which the
Q&A groups correspond; and updating a knowledge base of a Q&A system according

to the statement pairs. By splitting a session into finer grains, the present
application
achieves update of the knowledge base of the Q&A system according to
historical Q&A
records, and solves prior-art problems that questioning statements and
answering
statements included in human session data cannot be analyzed and mined,
whereby
update of the knowledge base is slow, and success rate of response is
adversely affected.
BRIEF DESCRIPTION OF THE DRAWINGS
[0056] In order to more clearly describe the technical solutions in the
embodiments of the
present invention, drawings required for the illustration of the embodiments
will be
briefly introduced below. Apparently, the drawings described below are merely
directed
7
Date Regue/Date Received 2022-06-29

to some embodiments of the present invention, and it is possible for persons
ordinarily
skilled in the art to acquire other drawings without spending creative effort
in the
process based on these drawings.
[0057] Fig. 1 is a flowchart illustrating a session process provided by an
embodiment of the
present application;
[0058] Fig. 2 is a flowchart illustrating Q&A group splitting provided by an
embodiment of
the present application;
[0059] Fig. 3 is a flowchart illustrating merging of text segments provided by
an embodiment
of the present application;
[0060] Fig. 4 is a flowchart illustrating merging of questioning statements
provided by an
embodiment of the present application;
[0061] Fig. 5 is a flowchart illustrating the method provided by an embodiment
of the present
application;
[0062] Fig. 6 is a view illustrating the structure of the device provided by
an embodiment of
the present application; and
[0063] Fig. 7 is a view illustrating the structure of the electronic equipment
provided by an
embodiment of the present application.
DETAILED DESCRIPTION OF THE INVENTION
[0064] In order to make more lucid and clear the objectives, technical
solutions and advantages
of the present invention, the technical solutions in the embodiments of the
present
invention will be clearly and comprehensively described below with reference
to the
accompanying drawings in the embodiments of the present invention. Apparently,
the
embodiments as described are merely partial embodiments, rather than the
entire
embodiments, of the present invention. All other embodiments obtainable by
persons
ordinarily skilled in the art based on the embodiments in the present
invention without
spending any creative effort shall all be covered by the protection scope of
the present
invention.
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[0065] As recited in the Description of Related Art, the comprehensive degree
of the
knowledge base is the deciding factor affecting the response effect of the
intelligent
customer service system.
[0066] To realize analysis and mining of human session data, the present
application provides
a Q&A statement processing method capable of enhancing generation efficiency
of
Q&A pairs, and ensuring update efficiency of the knowledge base of such a Q&A
system as intelligent customer service.
[0067] Embodiment 1
[0068] Specifically, as shown in Fig. 1, the process of analyzing and mining
dialogue
statements between customer service and a user according to the Q&A statement
processing method provided by an embodiment of the present application
includes the
following.
[0069] S10 ¨ obtaining a session record to be processed, and preprocessing the
obtained
session record.
[0070] Specifically, the process of preprocessing the session record includes
the following.
[0071] S 1 1 - rectifying any wrong word included in the session record
according to a preset
rectifying rule.
[0072] The session record can include speech statements and text statements.
When the session
record is directed to text statements, the principal wrong words are homonyms;
when
the session record is directed to speech statements, it is firstly required to
convert the
speech statements into text statements through the speech recognition
technique, the
main reason for generating wrong words is the imprecise speech recognition, so
the
corresponding wrong words are not only homonyms but also the words that are
similar
or identical in pronunciation. Accordingly, the language model and word
frequency
features are combined in the embodiments of the present application,
corresponding
rectifying rules are provided for speech statements and text statements
respectively, and
wrong words can be rectified according to the corresponding rectifying rules.
9
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[0073] S120 ¨ performing a purification operation on all characters included
in the session
record.
[0074] Specifically, the purification operation includes removing irrelevant
characters such as
preset useless punctuations and preset stop words, thereafter recognizing
irrelevant
information contained in each text statement such as commodity names and
placenames,
etc., and normalizing the irrelevant information to corresponding preset
characters
according to the type to which the irrelevant information corresponds.
[0075] S130 ¨recognizing a dialogue intent to which the questioning statement
sent by each
user corresponds by employing a preset classifier algorithm.
[0076] S200 ¨ splitting the preprocessed session record into Q&A groups
according to a preset
Q&A splitting rule.
[0077] It is possible to define the session record of a user with customer
service within one day
as a segment of dialogue. The session record can be firstly split into one or
more
dialogue(s), and the dialogue(s) is/are then split into Q&A groups.
[0078] As can be known from historical data analysis of users, a user usually
consults about
the same type of questions within a preset period of time, if the customer
service has
replied thereto, then the user would usually consult about different questions
next time
in dialogue with the customer service.
[0079] Based on the above features, it is possible to split a segment of
dialogue into one or
more Q&A group(s) according to time unit and splitting strategy, as shown in
Fig. 2,
such a splitting process includes the following.
[0080] S210¨ traversing the dialogue according to a temporal sequence of
generation times.
[0081] S220¨ screening questioning statements to be processed of a user when
the questioning
statements to be processed are traversed, and eliminating any questioning
statement to
be processed of the user that does not conform to a preset condition.
[0082] It is possible to define that each Q&A group starts with a questioning
statement (marked
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Date Regue/Date Received 2022-06-29

as Q) of the user and ends with an answering statement (marked as A) of the
customer
service.
[0083] Specifically, it is possible to eliminate any questioning statement
with eliminable intent
and irrelevant to business whose number of characters is smaller than a preset
number
threshold or whose intent is judged by the preset classifier algorithm as
chitchat intent.
[0084] S221 ¨ determining whether a questioning statement to be processed is
to be merged
with the antecedent questioning statement of the questioning statement to be
processed
according to a preset merging rule.
[0085] Specifically, when the interval time between the antecedent questioning
statement of
the questioning statement to be processed and the questioning statement to be
processed
exceeds a corresponding preset time threshold and/or when the sentence pattern
of the
antecedent answering statement of the questioning statement to be processed is
a preset
sentence pattern, the questioning statement to be processed can be merged with
its
antecedent questioning statement.
[0086] The antecedent questioning statement is a questioning statement that is
temporally
antecedent to the statement to be processed and with the shortest interval
time to the
statement to be processed. The antecedent answering statement is an answering
statement that is temporally antecedent to the statement to be processed and
with the
shortest interval time to the statement to be processed.
[0087] When the interval time between the antecedent questioning statement of
the questioning
statement to be processed and the questioning statement to be processed
exceeds a
corresponding preset time threshold, it can be judged that there is no
relevancy between
the antecedent questioning statement and the questioning statement to be
processed, so
a new Q&A group can be generated according to the questioning statement to be
processed. When the corresponding preset time threshold is not exceeded, it
can be
judged that there is relevancy between the antecedent questioning statement
and the
questioning statement to be processed, and the questioning statement to be
processed
can be merged in the Q&A group to which the antecedent questioning statement
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corresponds.
[0088] Preset sentence patterns include statements that guide the user to
further respond to
responses made by the customer service, usually include asks in reply by
customer
service to indefinite expressions of users, or sentence patterns asking for
essential
information from the user, such as "please provide your mobile phone number",
etc.
When an antecedent answering statement is of a preset sentence pattern, no
matter how
long the interval time is between the statement to be processed and the
antecedent
questioning statement or the antecedent answering statement, it can all be
considered
that the statement sent by the user after the antecedent answering statement
is made in
reply to this antecedent answering statement and is relevant to this
antecedent
answering statement, rather than a new, independent question. Therefore, the
questioning statement to be processed can be merged with the antecedent
questioning
statement.
[0089] When the antecedent answering statement of the questioning statement to
be processed
is of a preset sentence pattern, the questioning statement to be processed can
be merged
with the antecedent questioning statement, and the questioning statement to be

processed is merged in the Q&A group to which the antecedent questioning
statement
corresponds.
[0090] S230 ¨ eliminating any answering statement whose number of characters
is smaller
than the preset number threshold when the answering statements of the customer

service are traversed, and determining any answering statement remaining after
the
elimination as an answering statement to be processed.
[0091] S231 ¨ merging the answering statement to be processed with an
antecedent answering
statement when the antecedent statement of the answering statement to be
processed is
an answering statement of the customer service, and storing the merged
answering
statement to the Q&A group to which the antecedent questioning statement
corresponds.
[0092] The antecedent statement is a statement that is temporally antecedent
to the statement
to be processed and with the shortest interval time to the statement to be
processed.
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[0093] S232 ¨ merging the answering statement to be processed in the Q&A group
to which
the questioning statement corresponds when the antecedent statement of the
answering
statement to be processed is a questioning statement of the user.
[0094] Through the above process of splitting the dialogue into Q&A groups,
the processing
result includes three types of Q&A groups, including:
[0095] the circumstance in which one question corresponds to a segment of
reply, and this
circumstance is marked as QA;
[0096] the circumstance in which plural questions correspond to a segment of
reply, that is to
say, the user asks plural questions and the customer service replies with a
segment of
words, and this circumstance is marked as QQA; and
[0097] the circumstance in which plural questions correspond to plural
replies, that is to say,
several rounds of communication are carried out between the user and the
customer
service in a short time, and this circumstance is marked as QAQA.
[0098] The corresponding type can be determined according to the numbers of
answering
statements and questioning statements included in each Q&A group, and can be
processed according to the corresponding processing rule. The above process
includes
the following.
[0099] S310 ¨ splitting the questioning statements included in the Q&A group
each into text
segments when the Q&A group is of the QA type.
[0100] QA is a standard input form of the subsequent algorithm, but original
questions of the
QA type are rather complicated, as a questioning statement of the user might
contain
two or more questions, for example, "How would it be if Willful Pay is
overdue? And
how much will be the overdue interest rate?". However, one standard question
is only
meant to express one question in the knowledge base, and splitting is
therefore needed.
[0101] An auxiliary algorithm is required during splitting to judge whether
two segments of
words are directed to one question or to two questions. The auxiliary
algorithm can be
a binary classifier, inputs to the binary classifier are two statements, and
the task thereof
13
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is to judge whether the two statements are describing the same and single
question or
different questions. Any random model can be employed to realize the binary
questions.
Preferably, since the model bert can predict during the process of pretraining
as to
whether the input two statements are directed to the context of the same and
single
statement or topics irrelevant to each other, it is naturally suited to the
above task, and
bert can therefore be employed to serve as the classifier, and fine-tuning
training can
be performed under the task.
[0102] S311 ¨ processing the text segments from front to back, and merging any
text segment
whose number of characters is smaller than the preset number threshold in the
posterior
text segment of this text segment; and/or
[0103] merging any text segment pertaining to the same intent class as the
corresponding
posterior text segment or pertaining to a preset merging intent class in the
posterior text
segment of this text segment.
[0104] The posterior text segment indicates a text segment following and
immediately adjacent
to the text segment being processed.
[0105] Specifically, the above classifier algorithm can be employed to merge
any text segment
judged as pertaining to the same intent class as the posterior text segment or
pertaining
to such a preset merging intent class as a chitchat class in the posterior
text segment.
[0106] S320 ¨ sequentially obtaining a preset number of adjacent text segments
through a
sliding window, and predicting whether the obtained text segments belong to
the same
and single question by means of the binary classifier algorithm.
[0107] As shown in Fig. 3, text segments predicted to belong to the same
question can be
merged into one questioning statement, text segments predicted to belong to
different
questions can be split into two different questioning statements, and the
sequentially
posterior text segment will continue to participate in the subsequent
predicting process.
[0108] Through the above splitting process, Q&A groups of the QA type not
belonging to the
same and single question can be converted to Q&A groups of the QQA type, while
14
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Q&A groups of the QA type whose all text segments belong to the same and
single
question are split into a QA statement pair that only includes one questioning
statement
and one answering statement.
[0109] S320 ¨ traversing the questioning statements included in a Q&A group
and judging
whether each questioning statement and its antecedent questioning statement
belong to
the same question when the Q&A group is of the QQA type.
[0110] When the user sends a questioning statement to the customer service,
meaningless
pause of sentence might be generated, whereby the same question is split into
two
questioning statements, such as "May I venture to ask" and "how to pay back".
In some
other embodiments, there is also the circumstance in which the user actually
raises two
questions but the customer service makes reply by the same segment of response

statement.
[0111] To recognize to which circumstance a Q&A group of the QQA type
specifically pertains,
it is possible to judge through the aforementioned binary classification
algorithm.
Specifically, the questioning statement can be split into text segments, and
any text
segment whose number of characters included is smaller than the preset number
threshold or pertaining to the preset merging intent is directly merged with
the
antecedent questioning statement, or the questioning statement and the
antecedent
questioning statement are input together in the binary classification
algorithm to judge
whether they belong to the same question.
[0112] As shown in Fig. 4, when it is judged any text segment and its
antecedent questioning
statement belong to the same question, they can be merged into one questioning

statement; when it is recognized that they do not belong to the same question,
the
questioning statement can be split into new questioning statements.
[0113] After all questioning statements have been traversed, if the statements
that remain are
only one answering statement and one questioning statement, they can be
determined
as a QA statement pair. If the statements that remain are more than one
questioning
statement and one answering statement, the questioning statements and the
answering
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statements can be combined in pairs to generate corresponding QA statement
pairs. For
instance, when the remaining statements include questioning statement Ql,
questioning
statement Q2, and answering statement Al, statement pairs as generated then
include
statement pair Q1A1 and statement pair Q2A1.
[0114] S330 ¨ combining all questioning statements and answering statements in
pairs to
generate corresponding OA statement pairs when the Q&A group is of the QAQA
type.
[0115] The great deal of interaction between the user and the customer service
in a short time
can be usually split into plural groups of QA statement pairs. But there are
some special
cases, for instance, there is the guiding statement by the customer service as
previously
mentioned, so it is impossible to judge whether the questioning statements and
the
answering statements of the user and the customer service correspond to one
another
on a one-by-one basis.
[0116] To fully mine the problems of this type, answering statements and
questioning
statements can be directly combined in pairs with respect to Q&A groups of the
QAQA
type. For instance, if there are three answering statements and three
questioning
statements, 9 combination modes will be generated out of the different Qs and
different
As.
[0117] S400 ¨ clustering the statements by employing a preset clustering
algorithm, generating
statement pair groups, and determining the number of questioning statements
included
in each statement pair group.
[0118] Clustering as made public in the embodiments of the present application
means to
incorporate similar questions together to constitute a cluster. Since users
might raise
repetitive questions, the objective of this operation is to place similar
questions in the
same and single cluster, so that it suffices to screen out therefrom only one
or several
representative questioning-answering pair(s) during subsequent manual
selection or
machine screening.
[0119] It is possible to calculate text distance metrics amongst the statement
pairs via a text
16
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matching algorithm, and to determine whether the statement pairs belong to the
same
statement pair group according to the text distance metrics.
[0120] The text matching algorithm is an algorithm that calculates the
similarity degree of two
texts. Considering that the clustered objects are mostly questions outside the
knowledge
base, this means the supervised text matching algorithm trained on the basis
of the
original marking data in the knowledge base is of little effect. An
unsupervised text
matching algorithm, word mover's distance (WMD), can hence be employed. After
the
text distance metrics have been determined, any clustering algorithm can be
applied to
determine whether statement pairs belong to the same statement pair group,
considering
the advantage that the number of clusters is not needed to be predetermined in

hierarchical clustering, hierarchical clustering is preferentially selected.
[0121] S410 ¨ determining matching degrees between the questioning statements
and the
answering statements included in the statement pair groups according to a
preset
similarity algorithm.
[0122] In all the QA pairs, there might appear invalid QA pairs caused by
imprecise splitting,
and also the circumstance in which answers are not pertinent to the questions
asked due
to negligence of the customer service, and such invalid QA statement pairs
should be
removed after filtration.
[0123] Filtration of the QA statement pairs is mainly decided by matching
degrees of questions
and answers, QA statement pairs whose matching degrees between questioning
statements and answering statements satisfy a preset condition can be
remained, while
QA statement pairs whose matching degrees between questioning statements and
answering statements do not satisfy the condition are eliminated and filtered
out.
[0124] The above matching process is also a text matching process, since
matching of
questions and answers possesses certain generality, a set of supervised
algorithms can
be trained on the basis of existing knowledge base data to perform similarity
calculation.
[0125] S420 - determining a weight to which each statement pair group
corresponds according
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to the corresponding matching degrees and the numbers of questioning
statements
included in the statement pair groups.
[0126] Insofar as a knowledge base is concerned, not all questions have the
same degree of
importance, so frequently asked questions should have higher priorities to be
maintained to the knowledge base. At the same time, the more accurate the
answers to
the collected questions are, the more valuable will they be for maintenance to
the
knowledge base. After the statement pairs have been sorted, more important
questions
therein can be preferentially maintained while some less valuable questions
are
neglected, whereby maintenance efficiency can be enhanced to a greater extent.
[0127] Frequencies by which questions are asked can be measured by the number
of questions
under each cluster obtained in the above clustering process, and accuracy of
answers
can be measured by matching degrees of questions and answers in the filtering
process.
[0128] The corresponding sorting weight can be derived by normalizing two
values and
thereafter weighting and accumulating the same, and corresponding statement
pairs can
be sequentially obtained according to the sorting weights during subsequent
maintenance of the knowledge base, further screened and processed manually or
by
machine, and maintained in the knowledge base.
[0129] The Q&A statement processing method provided by the embodiment of the
present
application realizes automated processing of statement pairs, alleviates
workload of
business personnel, greatly reduces operation and maintenance costs, greatly
lowers the
threshold to answer configuration since the answers are not entirely dependent
upon
human conception, and reduces training cost of operation and maintenance
personnel.
[0130] Embodiment 2
[0131] Corresponding to the foregoing embodiment, as shown in Fig. 5, the
present application
provides a Q&A statement processing method that comprises the following steps.
[0132] 510 - obtaining a session record to be processed, wherein the session
record includes at
least two statements, and the statements include questioning statements sent
by
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questioners and answering statements sent by answerers.
[0133] 520 - splitting the session record into corresponding Q&A groups
according to a preset
Q&A splitting rule, wherein the Q&A groups include at least one questioning
statement
and at least one answering statement.
[0134] Preferably, each statement has a corresponding generation time, and the
step of splitting
the session record into corresponding Q&A groups according to a preset Q&A
splitting
rule includes:
[0135] 521 - sequentially traversing the session record according to the
generation time of each
statement;
[0136] 522 - judging, when the statement traversed is a questioning statement,
whether the
traversed questioning statement and the antecedent questioning statement of
the
traversed questioning statement belong to the same Q&A group according to a
sentence
pattern of the antecedent answering statement of the traversed questioning
statement
and/or according to an interval time to the antecedent questioning statement
of the
traversed questioning statement; and
[0137] 523 - determining, when the statement traversed is an answering
statement, that the
traversed answering statement belongs to the Q&A group to which the antecedent

questioning statement of the traversed answering statement corresponds.
[0138] Preferably, before the step of splitting the session record into
corresponding Q&A
groups according to a preset Q&A splitting rule, the method further comprises:
[0139] 524 - rectifying any wrong word included in the session record
according to a preset
rectifying rule; and
[0140] 525 - performing a normalizing process on the rectified session record.
[0141] Preferably, before the step of splitting the session record into
corresponding Q&A
groups according to a preset Q&A splitting rule, the method further comprises:
[0142] 526 - recognizing the intent class to which each questioning statement
included in the
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session record corresponds by employing a preset classifier algorithm and
eliminating
any questioning statement to which a preset irrelevant intent class
corresponds as
included in the session record.
[0143] 530 - determining a processing rule to which the Q&A groups correspond
according to
the number of the questioning statement(s) and the number of the answering
statement(s)
included in the Q&A groups.
[0144] 540 - splitting the Q&A groups into corresponding statement pairs
according to the
processing rule to which the Q&A groups correspond.
[0145] Preferably, the step of splitting the Q&A groups into corresponding
statement pairs
according to the processing rule to which the Q&A groups correspond includes:
[0146] 541 - splitting, when the number of the questioning statements included
in the Q&A
group does not exceed a first preset threshold, the questioning statements
each into at
least two text segments according to preset signs included in the questioning
statements;
[0147] 542 - predicting whether two adjacent text segments belong to the same
question by
employing a preset binary classifier;
[0148] 543 - generating corresponding questioning statements respectively
according to text
segments predicted to belong to the same question; and
[0149] 544 - generating corresponding statement pairs according to all the
questioning
statements as generated and the answering statements included in the Q&A
group.
[0150] Preferably, before the step of predicting whether two adjacent text
segments belong to
the same question by employing a preset binary classifier, the method further
comprises:
[0151] 545 - traversing the text segments, and merging the traversed text
segments with
corresponding posterior text segments when the number of characters of the
traversed
text segments is smaller than a second preset threshold; and/or
[0152] 546 - employing a preset classifier algorithm to merge the traversed
text segments with
corresponding posterior text segments when the traversed text segments and the
Date Regue/Date Received 2022-06-29

corresponding posterior text segments belong to the same intent class or when
the
traversed text segments belong to a preset merging intent class.
[0153] Preferably, the step of splitting the Q&A groups into corresponding
statement pairs
according to the processing rule to which the Q&A groups correspond includes:
[0154] 547 - combining, when the numbers of the questioning statements and the
answering
statements included in the Q&A group both exceed the first preset threshold,
the
questioning statements and the answering statements included in the Q&A group,
and
generating the corresponding statement pairs.
[0155] Preferably, the step of splitting the Q&A groups into corresponding
statement pairs
according to the processing rule to which the Q&A groups correspond includes:
[0156] 548 - employing, when the number of the answering statements included
in the Q&A
group does not exceed the first preset threshold and the number of the
questioning
statements as included exceeds the first preset threshold, the preset binary
classifier to
predict whether the questioning statements as included and antecedent
questioning
statements of the questioning statements as included belong to the same
question;
[0157] 549 - merging, when there are the questioning statements that belong to
the same
question, the questioning statements that belong to the same question and
generating
the corresponding statement pairs according to all the merged questioning
statements
and the answering statements; and
[0158] generating the corresponding statement pairs according to all the
questioning
statements and answering statements included in the Q&A group, when there are
no
questioning statements that belong to the same question.
[0159] 550 ¨ updating a knowledge base of a Q&A system according to the
statement pairs.
[0160] Preferably, the step of updating a knowledge base of a Q&A system
according to the
statement pairs includes:
[0161] 551 - clustering the statement pairs by employing a preset clustering
algorithm,
21
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generating statement pair groups, and determining the number of the
questioning
statements included in each statement pair group;
[0162] 552 - determining matching degrees between the questioning statements
and the
answering statements included in the statement pair groups according to a
preset
similarity algorithm;
[0163] 553 - determining a weight to which each statement pair group
corresponds according
to the corresponding matching degrees and the numbers of questioning
statements
included in the statement pair groups; and
[0164] 554 - sequentially updating the knowledge base of the Q&A system
according to the
weight to which each statement pair group corresponds.
[0165] Embodiment 3
[0166] Corresponding to Embodiment 1 and Embodiment 2, as shown in Fig. 6, the
present
application provides a Q&A statement processing device that comprises:
[0167] an obtaining module 610, for obtaining a session record to be
processed, wherein the
session record includes at least two statements, and the statements include
questioning
statements sent by questioners and answering statements sent by answerers;
[0168] a splitting module 620, for splitting the session record into
corresponding Q&A groups
according to a preset Q&A splitting rule, wherein the Q&A groups include at
least one
questioning statement and at least one answering statement;
[0169] a judging module 630, for determining a processing rule to which the
Q&A groups
correspond according to the number of the questioning statement(s) and the
number of
the answering statement(s) included in the Q&A groups; wherein
[0170] the splitting module 620 is further employed for splitting the Q&A
groups into
corresponding statement pairs according to the processing rule to which the
Q&A
groups correspond; and
[0171] an updating module 640, for updating a knowledge base of a Q&A system
according to
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the statement pairs.
[0172] Preferably, each statement has a corresponding generation time, and the
splitting
module 640 can be further employed for sequentially traversing the session
record
according to the generation time of each statement; judging, when the
statement
traversed is a questioning statement, whether the traversed questioning
statement and
the antecedent questioning statement of the traversed questioning statement
belong to
the same Q&A group according to a sentence pattern of an antecedent answering
statement of the traversed questioning statement and/or according to an
interval time to
the antecedent questioning statement of the traversed questioning statement;
and
determining, when the statement traversed is an answering statement, that the
traversed
answering statement belongs to the Q&A group to which an antecedent
questioning
statement of the traversed answering statement corresponds.
[0173] Preferably, the splitting module 630 can be further employed for
splitting, when the
number of the questioning statements included in the Q&A group does not exceed
a
first preset threshold, the questioning statements each into at least two text
segments
according to preset signs included in the questioning statements;
[0174] predicting whether two adjacent text segments belong to the same
question by
employing a preset binary classifier;
[0175] generating corresponding questioning statements respectively according
to text
segments predicted to belong to the same question; and
[0176] generating corresponding statement pairs according to all the
questioning statements as
generated and the answering statements included in the Q&A group.
[0177] Preferably, the splitting module 630 can be further employed for
employing, when the
number of the answering statements included in the Q&A group does not exceed
the
first preset threshold and the number of the questioning statements as
included exceeds
the first preset threshold, the preset binary classifier to predict whether
the questioning
statements as included and antecedent questioning statements of the
questioning
23
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statements as included belong to the same question;
[0178] merging, when there are the questioning statements that belong to the
same question,
the questioning statements that belong to the same question and generating the

corresponding statement pairs according to all the merged questioning
statements and
the answering statements; and
[0179] generating the corresponding statement pairs according to all the
questioning
statements and answering statements included in the Q&A group, when there are
no
questioning statements that belong to the same question.
[0180] Preferably, the splitting module 630 can be further employed for
traversing the text
segments, and merging the traversed text segments with corresponding posterior
text
segments when the number of characters of the traversed text segments is
smaller than
a second preset threshold; and/or merging the traversed text segments with
corresponding posterior text segments by employing a preset classifier
algorithm when
the traversed text segments and the corresponding posterior text segments
belong to the
same intent class or when the traversed text segments belong to a preset
merging intent
class.
[0181] Preferably, the splitting module 630 can be further employed for
combining, when the
numbers of the questioning statements and the answering statements included in
the
Q&A group both exceed the first preset threshold, the questioning statements
and the
answering statements included in the Q&A group, and generating the
corresponding
statement pairs.
[0182] Preferably, the updating module 640 can be further employed for
clustering the
statement pairs by employing a preset clustering algorithm, generating
statement pair
groups, and determining the number of the questioning statements included in
each
statement pair group; determining matching degrees between the questioning
statements and the answering statements included in the statement pair groups
according to a preset similarity algorithm; determining a weight to which each

statement pair group corresponds according to the corresponding matching
degrees and
24
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the numbers of questioning statements included in the statement pair groups;
and
sequentially updating the knowledge base of the Q&A system according to the
weight
to which each statement pair group corresponds.
[0183] Preferably, the splitting module 630 can be further employed for
rectifying any wrong
word included in the session record according to a preset rectifying rule; and
performing
a normalizing process on the rectified session record.
[0184] Preferably, the splitting module 630 can be further employed for
recognizing the intent
class to which each questioning statement included in the session record
corresponds
by employing the preset classifier algorithm and eliminating any questioning
statement
to which a preset irrelevant intent class corresponds as included in the
session record.
[0185] Embodiment 4
[0186] Corresponding to all the foregoing embodiments, an embodiment of the
present
application provides an electronic equipment that comprises:
[0187] one or more processor(s); and a memory, associated with the one or more
processor(s)
and used for storing a program instruction. The program instruction executes
the
following operations when it is read and executed by the one or more
processor(s):
[0188] before the step of splitting the session record into corresponding Q&A
groups according
to a preset Q&A splitting rule, the method further comprises:
[0189] recognizing the intent class to which each questioning statement
included in the session
record corresponds by employing the preset classifier algorithm and
eliminating any
questioning statement to which a preset irrelevant intent class corresponds as
included
in the session record.
[0190] Fig. 6 exemplarily illustrates the framework of the electronic
equipment that can
specifically include a processor 1510, a video display adapter 1511, a
magnetic disk
driver 1512, an input/output interface 1513, a network interface 1514, and a
memory
1520. The processor 1510, the video display adapter 1511, the magnetic disk
driver
1512, the input/output interface 1513, the network interface 1514, and the
memory 1520
Date Regue/Date Received 2022-06-29

can be communicably connected with one another via a communication bus 1530.
[0191] The processor 1510 can be embodied as a general CPU (Central Processing
Unit), a
microprocessor, an ASIC (Application Specific Integrated Circuit), or one or
more
integrated circuit(s) for executing relevant program(s) to realize the
technical solutions
provided by the present application.
[0192] The memory 1520 can be embodied in such a form as an ROM (Read Only
Memory),
an RAM (Random Access Memory), a static storage device, or a dynamic storage
device. The memory 1520 can store an operating system 1521 for controlling the

running of an electronic equipment 1500, and a basic input/output system
(BIOS) 1522
for controlling lower-level operations of the electronic equipment 1500. In
addition, the
memory 1520 can also store a web browser 1523, a data storage administration
system
1524, and an icon font processing system 1525, etc. The icon font processing
system
1525 can be an application program that specifically realizes the
aforementioned
various step operations in the embodiments of the present application. To sum
it up,
when the technical solutions provided by the present application are to be
realized via
software or firmware, the relevant program codes are stored in the memory
1520, and
invoked and executed by the processor 1510.
[0193] The input/output interface 1513 is employed to connect with an
input/output module to
realize input and output of information. The input/output module can be
equipped in
the device as a component part (not shown in the drawings), and can also be
externally
connected with the device to provide corresponding functions. The input means
can
include a keyboard, a mouse, a touch screen, a microphone, and various sensors
etc.,
and the output means can include a display screen, a loudspeaker, a vibrator,
an
indicator light etc.
[0194] The network interface 1514 is employed to connect to a communication
module (not
shown in the drawings) to realize intercommunication between the current
device and
other devices. The communication module can realize communication in a wired
mode
(via USB, network cable, for example) or in a wireless mode (via mobile
network, WIFI,
26
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Bluetooth, etc.).
[0195] The bus 1530 includes a passageway transmitting information between
various
component parts of the device (such as the processor 1510, the video display
adapter
1511, the magnetic disk driver 1512, the input/output interface 1513, the
network
interface 1514, and the memory 1520).
[0196] Additionally, the electronic equipment 1500 may further obtain
information of specific
collection conditions from a virtual resource object collection condition
information
database 1541 for judgment on conditions, and so on.
[0197] As should be noted, although merely the processor 1510, the video
display adapter 1511,
the magnetic disk driver 1512, the input/output interface 1513, the network
interface
1514, the memory 1520, and the bus 1530 are illustrated for the aforementioned

equipment, the equipment may further include other component parts
prerequisite for
realizing normal running during specific implementation. In addition, as can
be
understood by persons skilled in the art, the aforementioned equipment may as
well
only include component parts necessary for realizing the solutions of the
present
application, without including the entire component parts as illustrated.
[0198] As can be known through the description to the aforementioned
embodiments, it is
clearly learnt by person skilled in the art that the present application can
be realized
through software plus a general hardware platform. Based on such
understanding, the
technical solutions of the present application, or the contributions made
thereby over
the state of the art, can be essentially embodied in the form of a software
product, and
such a computer software product can be stored in a storage medium, such as an

ROM/RAM, a magnetic disk, an optical disk etc., and includes plural
instructions
enabling a computer equipment (such as a personal computer, a cloud server, or
a
network device etc.) to execute the methods described in various embodiments
or some
sections of the embodiments of the present application.
[0199] The various embodiments are progressively described in the Description,
identical or
similar sections among the various embodiments can be inferred from one
another, and
27
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each embodiment stresses what is different from other embodiments.
Particularly, with
respect to the system or system embodiment, since it is essentially similar to
the method
embodiment, its description is relatively simple, and the relevant sections
thereof can
be inferred from the corresponding sections of the method embodiment. The
system or
system embodiment as described above is merely exemplary in nature, units
therein
described as separate parts can be or may not be physically separate, parts
displayed as
units can be or may not be physical units, that is to say, they can be located
in a single
site, or distributed over a plurality of network units. It is possible to base
on practical
requirements to select partial modules or the entire modules to realize the
objectives of
the embodied solutions. It is understandable and implementable by persons
ordinarily
skilled in the art without spending creative effort in the process.
[0200] What the above describes is merely directed to preferred embodiments of
the present
invention, and is not meant to restrict the present invention. Any
modification,
equivalent substitution, and improvement made within the spirit and scope of
the
present invention shall all be covered by the protection scope of the present
invention.
28
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Representative Drawing