PRODUCING TRAINING SETS FOR MACHINE LEARNING METHODS BY PERFORMING DEEP SEMANTIC ANALYSIS OF NATURAL LANGUAGE TEXTS

1 . A method, comprising:

performing, by a computer system, a lexico-morphological analysis of a natural language text comprising a plurality of tokens, to determine one or more lexical and grammatical attributes associated with each token of the plurality of tokens, each token comprising at least one natural language word;

performing a syntactico-semantic analysis of the natural language text to produce a plurality of syntactico-semantic structures representing the natural language text;

determining, using the syntactico-semantic structures, a plurality of syntactic and semantic attributes associated with the natural language text;

selecting, among the lexical, grammatical, syntactic and semantic attributes, a set of output attributes; and

producing an output text comprising a first attribute associated with a part of the natural language text and a second attribute associated with the part of the natural language text, wherein the first attribute specifies a category of an information object represented by the part of the natural language text and wherein the second attribute identifies a sub-category of the information object.

2 . The method of claim 1 , further comprising:

determining a degree of association of the part of natural language text with the category of the information object.

3 . The method of claim 2 , wherein determining the degree of association further comprises:

interpreting the syntactico-semantic structures using a set of production rules.

4 . The method of claim 2 , wherein determining the degree of association further comprises:

applying a classifier function to one or more values of the lexical, grammatical, syntactic and semantic attributes.

5 . The method of claim 2 , further comprising:

identifying one or more relationships between recognized informational objects to extract one or more facts represented by at least a fragment of the natural language text.

6 . The method of claim 5 , wherein identifying the relationships further comprises:

interpreting the syntactico-semantic structures using a set of production rules.

7 . The method of claim 5 , wherein identifying the relationships further comprises:

applying a classifier function to one or more values of the lexical, grammatical, syntactic and semantic attributes.

8 . The method of claim 1 , wherein the output set of attributes comprises a first alternative value for the first attribute and a second alternative value for the first attribute.

9 . The method of claim 8 , wherein the output set of attributes comprises a degree of association of the first alternative value with the first attribute.

10 . The method of claim 1 , wherein the output text is represented by an extensible markup language (XML) text.

11 . The method of claim 1 , wherein each syntactico-semantic structure of the plurality of syntactico-semantic structures is represented by a graph comprising a plurality of nodes corresponding to a plurality of semantic classes and a plurality of edges corresponding to a plurality of semantic relationships.

12 . A system, comprising:

a memory;

a processor, coupled to the memory, the processor configured to:

perform a lexico-morphological analysis of a natural language text comprising a plurality of tokens, to determine one or more lexical and grammatical attributes associated with each token of the plurality of tokens, each token comprising at least one natural language word;

perform a syntactico-semantic analysis of the natural language text to produce a plurality of syntactico-semantic structures representing the natural language text;

determine, using the syntactico-semantic structures, a plurality of syntactic and semantic attributes associated with the natural language text;

select, among the lexical, grammatical, syntactic and semantic attributes, a set of output attributes; and

produce an output text comprising a first attribute associated with a part of the natural language text and a second attribute associated with the part of the natural language text, wherein the first attribute specifies a category of an information object represented by the part of the natural language text and wherein the second attribute identifies a sub-category of the information object.

13 . The system of claim 12 , wherein the processor is further configured to:

determine a degree of association of the part of natural language text with the category of the information object.

14 . The system of claim 12 , wherein determining the degree of association further comprises:

interpreting the syntactico-semantic structures using a set of production rules.

15 . The system of claim 12 , wherein the output set of attributes comprises a first alternative value for the first attribute and a second alternative value for the first attribute.

16 . The system of claim 12 , wherein the output text is represented by an extensible markup language (XML) text.

17 . The system of claim 12 , wherein each syntactico-semantic structure of the plurality of syntactico-semantic structures is represented by a graph comprising a plurality of nodes corresponding to a plurality of semantic classes and a plurality of edges corresponding to a plurality of semantic relationships.

18 . A computer-readable non-transitory storage medium comprising executable instructions that, when executed by a computer system, cause the computer system to:

perform a lexico-morphological analysis of a natural language text comprising a plurality of tokens, to determine one or more lexical and grammatical attributes associated with each token of the plurality of tokens, each token comprising at least one natural language word;

perform a syntactico-semantic analysis of the natural language text to produce a plurality of syntactico-semantic structures representing the natural language text;

determine, using the syntactico-semantic structures, a plurality of syntactic and semantic attributes associated with the natural language text;

select, among the lexical, grammatical, syntactic and semantic attributes, a set of output attributes; and

produce an output text comprising a first attribute associated with a part of the natural language text and a second attribute associated with the part of the natural language text, wherein the first attribute specifies a category of an information object represented by the part of the natural language text and wherein the second attribute identifies a sub-category of the information object.

19 . The computer-readable non-transitory storage medium of claim 18 , wherein the output set of attributes comprises a first alternative value for the first attribute and a second alternative value for the first attribute.

20 . The computer-readable non-transitory storage medium of claim 18 , wherein each syntactico-semantic structure of the plurality of syntactico-semantic structures is represented by a graph comprising a plurality of nodes corresponding to a plurality of semantic classes and a plurality of edges corresponding to a plurality of semantic relationships.