Note: Descriptions are shown in the official language in which they were submitted.
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Description
Formalization of a natural language
Technical Field
The invention is about input of knowledge in a machine using a natural
language. It
can be used as a machine translator of a natural language.
Background Art
The most popular schemes are those in which machines interpret defined set of
words in a natural language - all artificial languages are of that type. There
are
attempts to define the grammatical meanings of the words. There are
developments in
which it is given the subject field for a given text and in that way it can
also be defined
the preferred meaning of a word and therefore to fulfill better results, for
example in a
machine translation. There are attempts to define the meaning of a word from
the other
words in the text and from the statistics for usage of the word among other
words.
There are attempts to set digital values from the same set to the words in a
given
natural language and to other natural language, so that the words from both
languages
with one and the same appropriated value to have alike meaning.
Disclosure of Invention
Technical Problem
It is not solved the problem of unambiguous interpreting of a natural language
from
a machine, which is a hindrance for input of knowledge and data in the machine
using
a natural language. A machine cannot be used for an official translation of a
document
because it is not a reliable way for a translation. It cannot be created a
text of a natural
language which has an unambiguous interpretation from different people but it
is really
important while writing textbooks or patent applications. A computer cannot be
programmed using a natural language because one sentence of a natural language
has
many possible meanings from a formal point of view, so grammatically true
sentences
can be interpreted in different ways. The existing human knowledge cannot be
used
optimally because there is no formalized way in which a machine interprets
directly
knowledge written in a natural language.
Technical Solution
The interpretation of a natural language always includes building of a machine
model of interpreted knowledge. The text in a natural language is interpreted
by
different means so that it can be defined the grammatical parts of speech, the
meaning
of the sentence and of the words in it. The problem is that there is no
backward relation
and a person cannot have influence on the formed model. This is that because
there is
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no base for comparison between the model and the text in a natural language.
So the
model is also a structure which cannot be interpreted in one way only.
Technical
essence of the offer is method for creating an unambiguous model. The model
formed
in this way can be interpreted in one unique way only.
The method has five steps.
In the first step it is made study of a grate number of languages as the
purpose is to
be defined the basis of notions that the human race uses. It has to be taken
into con-
sideration that a word in a natural language is not a basic notion. The basic
notion is
denotation of some entity or action. Usually with one and the same word in a
natural
language is denoted several different basic notions, so that the words have
different
meanings. The offer from the level of technics is to denote 'sluntze=l'
('sluntze' in
English means sun) and 'sun=l' can contribute to making a machine translation,
but it
cannot contribute to making a meaningful unambiguous translation. In this kind
of
systems the result from the translation can be of that kind: 'User rights =
prava na
narkomana' ('prava na narkomana' is in English the rights of drug addicted),
but in fact
in the given context 'user rights' means the rights of the customer. This kind
of
numerated words creates just an intermediate language with ambiguous meaning.
The
offer is to numerate the entities but not the words. The entities according to
the method
have unique names. The names can be numbers, but they can also be words from a
widely spread natural language. It has to be mentioned that a given word in a
natural
language can be used only in one way for denoting of an entity. In that way
'sluntze'
('sluntze' in English is sun) can have only the meaning - star, and for all
the other
meanings of the word'sluntze' it must be chosen other words. It should be
understood
that this king of naming the meanings influences in no way on the natural
language.
The entities according to the method are characterized with their
descriptions. The de-
scriptions of the entities are given in a natural language in the same way
which it is
done in a dictionary in a natural language. Each entity has a list of words
with which it
can be named in a natural language - something like a Dictionary Thesaurus but
for
entities not for words.
The structure about an entity that has an unique label - name or number, a de-
scription, and a list of words representing said entity in a natural language
is further
called basic notion.
The second step of the method is to be created the model of the text in a
natural
language using only basic notions . In this step of the method they are used
all ap-
plicable methods from background art which gives the ability to be defined
grammatical and semantic meanings of the words in the text and to be created
the
model. During the creation of the model it can be used global statistics for
the usage of
words in their different meanings or a local statistics for each user of the
method, It
can be used similar texts with already specified meaning of the words. Human
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translations of a given texts from one language into another can also be used
for
defining the basic notions used in the text in a natural language as the used
words in
translations are explored and they are compared to words from the original
text con-
sidering their meanings.
The third step of the method is a backward relation, to this step the created
model
in the second step is used as a base for generating a text in the same natural
language
in which the original text is. An operator has the ability to make changes in
the
generated model using computer program so that the generated model meets his
ex-
pectations for understanding of the text. This can be made with a direct
change in the
model as it is worked directly with represented entities, for example with a
tree of the
relations between the entities. This manner of work requires serious training.
In
another realization the change in the model can be done by the means of
attempt to
explain to the computer which entity should be changed. It is possible the
original text
to be compared with the generated text and to mark the differences between the
original and the generated text. For each marked word from a thesaurus
dictionary it
outputs a list of synonyms as it is possible to filter those synonyms that
have been
rejected as some with unappropriated meaning. The operator chooses from the
list with
synonyms and the process repeats in real time - so there is new generation and
there is
a possible new correction. The choice of synonyms however not always is enough
for
defining of a given entity. So it can be considered some means for change of
the inter-
pretation of the relationship between two basic notions in a given text. In
that way, a
relationship can be made using visual means for marking and identification.
For
example, it can be specified which the subject in the sentence is or which the
mean is
and which the explanation is. It is possible to be created a mean by which it
is
indicated the tense relations in the text. It is possible to be created means
to change the
external characteristics of a text so that the interpretation and generation
can be
managed easily. For example, it can be pointed the cases in which the true
inter-
pretation distinguishes from the standard one like playing with words and
sarcasm - in
that way it must be given both interpretations: the standard one and the
modified one,
according to the external characteristic, and they become part of an
unambiguous
model. It can be created many means of that kind aiming to make it possible
for a
medium educated person to show to the computer what he/she has in mind. The
aim is
to be achieved an unambiguous model which represents the meaning of the text
in the
most accurate way.
The forth step of the method - The generated unambiguous model of the text in
a
natural language is attached to the file containing the text in the natural
language. This
makes unambiguous interpretation of the text in the natural language which is
useful in
patent applications and in machine translation. When a text in a textbook is
created
using the method with attaching unambiguous model it is possible the computer
program to generate an explanation in a random level of complexity as it uses
the def-
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initions of the entities used in the text and as well a recursive usage of the
definitions
of the entities used when defining the entities in an upper level.
Fifth step of the method is usage of unambiguous models of texts in natural
language for machine learning and for creation of concepts and theories by a
machine
using the base of formalized knowledge got from the unambiguous models of the
texts
in a natural language.
Advantageous Effects
The application of the invention can be in a machine translation, in searching
for
knowledge, where searching is not in the base of words the text contains, as
it is in the
today's level of technics, but the searching is of similar unambiguous models
of the
searched text. It is possible to be made also a search using analysis of
unambiguous
models of the texts - so the explorer can answer a question like searching for
in-
formation about transferring property to foreign citizens according to the
Bulgarian
laws.
Best Moss.
Exemplar realization of the first step of the method
Using a computer program it is determined the basic notions of the language
and it
is examined the list of each words synonyms in the examined natural language.
The
definitions of each word of the language which are given in the dictionary are
compared to the definitions of its synonyms also given in the dictionary.
Comparison
of the definitions is made using simple comparison and searching in similar
texts. The
aim is to define the different meanings of a given word according to the
synonyms of
each meaning. In this way using comparison between the definition of each word
with
the definition of its synonym, given in the dictionary, are defined the
relevant similar
texts from both definitions - they form different meanings, named in this
method
"entities". The definition of an entity is usually formed by similar texts in
the def-
initions of both synonyms. When such an entity is found it is made a check in
the
database if it is not already registered a similar entity while comparing the
descriptions
of the registered entities with the description of the new entity. If the new
entity is not
already registered in the database, it i~ registered.
After automatic forming of the base of entities with their descriptions,
experts are
offered to name the entities and to specify their descriptions. To the
entities it is given
a list of words which can define them in certain conditions which depend on
the text
containing the word and on the external characteristic of the text like if the
text is
scientific or if the text is playing with words and so on. It is possible when
the base of
all entities is already available to be made the description of each entity
using an un-
ambiguous model of the description in a natural language. This can be done by
philologists who create an unambiguous model of-the entity's description using
the au-
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tomatically formed description in a natural language as they use the basic
notions of
the language. After finding the basic notions in a natural language, the next
natural
language uses the formed base of basic notions. It is easier philologists to
define how
in a certain language they can name the registered entities and eventually the
set of
entities which must be added to the base additionally. When an entity is added
to the
base philologists who look after the accordance of the natural languages
should be
informed so that they can give a proper name of the new entity, they are in
charge of. It
is possible the name of the new entity to be descriptive.
It is possible exploration of a second and so on natural language to be
automatized.
The, same procedure is set as this in the first explored language. It is made
a new base
from registered entities. The names which an entity from the new base can have
are
words from the second language. From a second language to first language
dictionary
it is found the possible translations of each name of an entity of the second
base. For
each translation - a word from the first language from the first base, it is
taken out the
entities which can be named with this word. It is made pseudo-translations of
the de-
scription of the entity in the second language as all the combinations of
substitutions of
each word of the description with all possible translations in the first
language are
generated. Pseudo-translations of the description of the entity from the
second
language are compared to the descriptions of the taken out entities of the
first base. It
is found and marked the best accordance. Each found accordance in this way
should be
approved by a philologist. After approval of an accordance the entity is
erased from
the second base. The list of names for this entity in the second language is
marked that
it is in the second language and it is added to the entity of the first base.
After
processing all accordances, those entities that are still in the second base
are either
registered as new entities in the first base or a human finds their accordance
in the first
base.
In official documents it must be achieved unity of the generated text in a
natural
language from the unambiguous model. This can be done at the cost of
simplification
of the generated text so in spite of the fact that it is possible from a
language point of
view to have multiple generations of a text in a natural language which have
the same
meaning and to represent the same knowledge holding by the unambiguous model
to
be achieved an unique generation. It is the job of the philologists to add to
the un-
ambiguous model so much characteristics of the text that are necessary for
achieving
an unique generation.
Such an approach is especially important for a translation of official
documents
from one language into another and particularly for patent applications.
On the other hand, in translations of literature it is better to have
multitude of gen-
erations of texts in a natural language from the unambiguous model and to be
chosen
the best one for a construction of the concrete language using statistical
data from
literature in the pdrticular language.
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Exemplar realization of the second step of the method
The text can be presented as a list of trees and each tree is one sentence of
the text.
It is possible to have relationships between the separate trees. Each element
of a tree is
an object which has additional characteristics which are extracted
automatically from
the text or are been added manually by an operator, A part of these
characteristics are
relationships between each element of the tree and the other elements of the
tree. Some
of the elements of the tree representing a sentence in the text, for example
the
pronouns, can have a relationship with the elements belonging to other trees.
The order
of the trees in the list is of an importance. It represents the order of the
sentences in the
original text and eventually in the generated text from the unambiguous model.
Exemplar realization of the third step of the method
It is created a superstructure of a text editor with additional abilities to
help the
changes in the automatically formed unambiguous model of the text to be made
easily.
For example the screen to be divided into three areas. First area is for the
whole
original text - an ordinary text editor. The second area is for a backward
relationship
when the unambiguous model has been created. In it it is the machine generated
text of
the processed sentence of the text. When holding the pointer of the mouse over
a
certain word from the machine generated text it is shown as a hint the
description of
the basic notion which is named with that word. The same sentence is marked
properly
in the original text. The third area is a tools bar for changing the
unambiguous model
which is applicable on the second area. These tools include the change of the
the in-
terpreted entity as giving a synonym of the word which is a synonym of another
entity
named by the word in hand. It is possible as a hint to be given the
description of the
basic notion named by the synonym. It includes means to chose a characteristic
of the
text such as playing with words, a jest, poetry or scientific text. It
includes defining the
exact meanings for substitution of the used pronouns, for example who in fact
He, She
is or which It is. The exact meaning can be defined within the range of the
whole text
as it sets the relationship given with a definite pronoun to the previous
sentences in the
text. The text is examined consecutively from the beginning to the end as it
is given all
needed characteristics and relationships so that it is formed an unambiguous
model. A
sentence is processed while a machine generation make a text which at least
has the
same meaning as the original text. The process consists of set of changes and
gen-
erations.
Exemplar realization of the forth step of the method
The generated unambiguous model for a given text is attached to the original
file.
Such an attachment can be made by many ways. It is possible in the original
file to be
added a link to the unambiguous model of the text. It is possible the file in
the original
text and the file of the unambiguous model to be written in one archive
package. It
-must have in mind that in a general text in a natural language is possible to
have
multiple formed unambiguous models. This is that way because the multitude of
inter-
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pretations of a given text in a natural language is filtered by a human -
operator, who
uses his/her own understanding so that he/she translates the text in the
natural language
in an unambiguous machine model. So it is possible to foresee attaching of a
text in a
natural language to many unambiguous models. When it is about a patent
application it
is naturally the object of protection to be only one unambiguous model of the
text of
the application the same as it has been applied.
Exemplar realization of the fifth step of the method
The unambiguous models of the texts of a natural language can give in to a
formal
processing. It is possible to be created different kinds of representation of
the un-
ambiguous model which are proper for different kinds of machine processing. Un-
ambiguous models can be defined as a new kind of computer software because
they
can be a subject to formal interpretation. In this way it can be realized a
machine
learning as it is dragged out facts and relationships from the unambiguous
models of
the texts in a natural language. It can be applied unambiguously and formally
all
mechanisms which are studied in the artificial intelligence. In this way the
traditional
software will be replaced with expert systems which contact with ordinary user
in a
natural language with easy addition of an unambiguous model and which give
services
for generation of applied software in accordance with the needs of the user.
Industrial Applicability
The disclosed methods are executed by a special computer software. A computer
program can be used by professionals to create and support the database with
basic
notions used by the human race. Another computer software can be used by all
users,
those creating and using unambiguous models of natural language texts. The
last
computer software must be able to make a connection to the database with basic
notions.
The methods can be used in machine translation from a natural language to
another
natural language or to artificial language e.g. program language. The methods
can be
used in searching and processing natural language.
Especially the application of the method is important in the field of patent
system
not only for unambiguous defining of the object of the protection and the
possibility
for automatized search and investigation but also for the possibility of a
machine
processing in the newest and valuable knowledge of the humanity which can be a
reason for automatic generation of a new knowledge for the humanity.
