EXTRACTING FACTS FROM NATURAL LANGUAGE TEXTS

1 . A method, comprising:

receiving, by a computing device, an identifier of a token comprised by a natural language text, wherein the token comprising at least one natural language word references a first information object;

receiving identifiers of a first plurality of words representing a first fact of a specified category of facts, wherein the first fact is associated with the first information object of a specified category of information objects;

identifying, within the natural language text, a second plurality of words; and

responsive to receiving a confirmation that the second plurality of words represents a second fact associated with a second information object of the specified category of information objects, modifying a parameter of a classifier function that produces a value reflecting a degree of association of a given semantic structure with a fact of the specified category of facts.

2 . The method of claim 1 , wherein identifying the second plurality of words further comprises:

performing semantico-syntactic analysis of the natural language text to produce a first plurality of semantic structures;

identifying a second plurality of semantic structures, each semantic structure of the second plurality of semantic structures representing a sentence comprising one or more words of the first plurality of words;

identifying, using the first plurality of semantic structures, a second token representing the second information object of the specified category of information objects;

identifying, among the first plurality of semantic structures, a second semantic structure that comprises an element representing the second token and that is similar to a first semantic structure of the second plurality of semantic structures in view of a certain similarity metric; and

identifying the second plurality of words as corresponding to the second semantic structure.

3 . The method of claim 2 , wherein identifying the second token representing information objects of the specified category of information objects further comprises:

determining a degree of association of the second token with the specified category of information objects by interpreting the first plurality of semantic structures using a set of production rules.

4 . The method of claim 2 , wherein identifying the second token representing information objects of the specified category of information objects further comprises:

determining a degree of association of the second token with the specified category of information objects by evaluating a second classifier function using one or more attributes of the second token.

5 . The method of claim 1 , further comprising:

using the classifier function to perform a natural language processing operation.

6 . The method of claim 1 , wherein receiving the identifier of the token is performed via a graphical user interface.

7 . The method of claim 1 , wherein receiving the identifiers of the first plurality of words is performed via a graphical user interface.

8 . The method of claim 1 , further comprising: pre-processing the natural language text in view of an auxiliary ontology reflecting a document structure associated with the natural language text.

9 . The method of claim 1 , further comprising:

receiving a second natural language text;

performing semantico-syntactic analysis of the second natural language text to produce a third plurality of semantic structures;

identifying, using the third plurality of semantic structures, a third token representing a third information object of the specified category of information objects;

identifying, among semantic structures of the third plurality of semantic structures, one or more semantic structures that comprise an element representing the third token; and

using the classifier function to identify, among the identified semantic structures, a third semantic structure that represents a third fact of the specified category of facts.

10 . The method of claim 9 , wherein identifying the third semantic structure further comprises:

determining a plurality of values produced by the classifier function;

selecting an optimal value among the determined plurality of values; and

identifying the third semantic structure as a semantic structure corresponding to the selected optimal value.

11 . The method of claim 1 , wherein the first named entity is provided by a first information object and the second named entity is provided by a second information object.

12 . A system, comprising:

a memory;

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

receive an identifier of a token comprised by a natural language text, wherein the token comprising at least one natural language word references a first information object;

receive identifiers of a first plurality of words representing a first fact of a specified category of facts, wherein the first fact is associated with the first information object of a specified category of information objects;

identify, within the natural language text, a second plurality of words; and

responsive to receiving a confirmation that the second plurality of words represents a second fact associated with a second information object of the specified category of information objects, modify a parameter of a classifier function that produces a value reflecting a degree of association of a given semantic structure with a fact of the specified category of facts.

13 . The system of claim 12 , wherein identifying the second plurality of words further comprises:

performing semantico-syntactic analysis of the natural language text to produce a first plurality of semantic structures;

identifying a second plurality of semantic structures, each semantic structure of the second plurality of semantic structures representing a sentence comprising one or more words of the first plurality of words;

identifying, using the first plurality of semantic structures, a second token representing the second information object of the specified category of information objects;

identifying, among the first plurality of semantic structures, a second semantic structure that comprises an element representing the second token and that is similar to a first semantic structure of the second plurality of semantic structures in view of a certain similarity metric; and

identifying the second plurality of words as corresponding to the second semantic structure.

14 . The system of claim 13 , wherein identifying the second token representing information objects of the specified category of information objects further comprises:

determining a degree of association of the second token with the specified category of information objects by interpreting the first plurality of semantic structures using a set of production rules.

15 . The system of claim 13 , wherein identifying the second token representing information objects of the specified category of information objects further comprises:

determining a degree of association of the second token with the specified category of information objects by evaluating a second classifier function using one or more attributes of the second token.

16 . The system of claim 12 , wherein receiving the identifier of the token is performed via a graphical user interface.

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

receive a second natural language text;

perform semantico-syntactic analysis of the second natural language text to produce a third plurality of semantic structures;

identify, using the third plurality of semantic structures, a third token representing a third information object of the specified category of information objects;

identify, among semantic structures of the third plurality of semantic structures, one or more semantic structures that comprise an element representing the third token; and

use the classifier function to identify, among the identified semantic structures, a third semantic structure that represents a third fact of the specified category of facts.

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

receive an identifier of a token comprised by a natural language text, wherein the token comprising at least one natural language word references a first information object;

receive identifiers of a first plurality of words representing a first fact of a specified category of facts, wherein the first fact is associated with the first information object of a specified category of information objects;

identify, within the natural language text, a second plurality of words; and

responsive to receiving a confirmation that the second plurality of words represents a second fact associated with a second information object of the specified category of information objects, modify a parameter of a classifier function that produces a value reflecting a degree of association of a given semantic structure with a fact of the specified category of facts.

19 . The computer-readable non-transitory storage medium of claim 18 , wherein identifying the second plurality of words further comprises:

performing semantico-syntactic analysis of the natural language text to produce a first plurality of semantic structures;

identifying a second plurality of semantic structures, each semantic structure of the second plurality of semantic structures representing a sentence comprising one or more words of the first plurality of words;

identifying, using the first plurality of semantic structures, a second token representing the second information object of the specified category of information objects;

identifying, among the first plurality of semantic structures, a second semantic structure that comprises an element representing the second token and that is similar to a first semantic structure of the second plurality of semantic structures in view of a certain similarity metric; and

identifying the second plurality of words as corresponding to the second semantic structure.

20 . The computer-readable non-transitory storage medium of claim 18 , further comprising executable instructions causing the computing device to:

receive a second natural language text;

perform semantico-syntactic analysis of the second natural language text to produce a third plurality of semantic structures;

identify, using the third plurality of semantic structures, a third token representing a third information object of the specified category of information objects;

identify, among semantic structures of the third plurality of semantic structures, one or more semantic structures that comprise an element representing the third token; and

using the classifier function to identify, among the identified semantic structures, a third semantic structure that represents a third fact of the specified category of facts.