EXTRACTING ENTITIES FROM NATURAL LANGUAGE TEXTS

1 . A method, comprising:

receiving, by a computing device, identifiers of a first plurality of word groups within a natural language text, each word group comprising one or more natural language words;

associating an object represented by each word group with a concept of an ontology;

identifying, within the natural language text, a second plurality of word groups, wherein each word group of the second plurality of word groups is associated with the concept of the ontology;

responsive to receiving a confirmation that a word group of the second plurality of word groups represents an object associated with the concept of the ontology, modifying a parameter of a classification model that produces a value reflecting a degree of association of a given object with the concept of the ontology.

2 . The method of claim 1 , wherein identifying the second plurality of word groups 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 at least one word group of the second plurality of word groups;

identifying, among the first plurality of semantic structures, a semantic structure that is similar to at least one semantic structure of the second plurality of semantic structures in view of a certain similarity metric; and

identifying a word group corresponding to the identified semantic structure from the second plurality of semantic structures as associated with the second plurality of word groups.

3 . The method of claim 1 , further comprising:

employing the classification model for extracting information from natural language texts.

4 . The method of claim 3 , further comprising:

utilizing the ontology for performing a natural language processing operation.

5 . The method of claim 1 , further comprising:

implementing a graphical user interface for receiving identifiers of the first plurality of word groups within a natural language text.

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

7 . The method of claim 1 , further comprising:

receiving a second natural language text;

performing semantico-syntactic analysis of the second natural language text;

using the classification model to identify, in view of the semantico-syntactic analysis of the second natural language text, a second semantic structure that represents a second object associated with the concept.

8 . The method of claim 7 , wherein identifying the second semantic structure further comprises:

determining a plurality of values produced by a classification model, each value reflecting a degree of association of the second semantic structure with a corresponding concept of the ontology;

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

associating the second semantic structure with a concept corresponding to the selected optimal value.

9 . A system, comprising:

a memory;

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

receive identifiers of a first plurality of word groups within a natural language text, each word group comprising one or more natural language words;

associate an object represented by each word group with a concept of an ontology;

identify, within the natural language text, a second plurality of word groups, wherein each word group of the second plurality of word groups is associated with the concept of the ontology;

responsive to receiving a confirmation that a word group of the second plurality of word groups represents an object associated with the concept of the ontology, modify a parameter of a classification model that produces a value reflecting a degree of association of a given object with the concept of the ontology.

10 . The system of claim 9 , wherein to identify the second plurality of word groups, the processor is further configured to:

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

identify a second plurality of semantic structures, each semantic structure of the second plurality of semantic structures representing a sentence comprising at least one word group of the first plurality of word groups;

identify, among the first plurality of semantic structures, a semantic structure that is similar to at least one semantic structure of the second plurality of semantic structures in view of a certain similarity metric; and

identify a word group corresponding to the identified semantic structure as associated with the second plurality of word groups.

11 . The system of claim 9 , wherein the processor is further configured to:

employ the classification model for expanding the ontology.

12 . The system of claim 11 , wherein the processor is further configured to:

utilize the ontology for performing a natural language processing operation.

13 . The system of claim 1 , further comprising:

a graphical user interface for receiving identifiers of the first plurality of word groups within a natural language text.

14 . The system of claim 1 , wherein the processor is further configured to:

receive a second natural language text;

perform semantico-syntactic analysis of the second natural language text;

use the classification model to identify, in view of the semantico-syntactic analysis of the second natural language text, a second semantic structure that represents a second object associated with the concept.

15 . The system of claim 14 , to identify the second semantic structure, the processor is further configured to:

determine a plurality of values produced by a classification model, each value reflecting a degree of association of the second semantic structure with a corresponding concept of the ontology;

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

associate the second semantic structure with a concept corresponding to the selected optimal value.

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

receive identifiers of a first plurality of word groups within a natural language text, each word group comprising one or more natural language words;

associate an object represented by each word group with a concept of an ontology;

identify, within the natural language text, a second plurality of word groups, wherein each word group of the second plurality of word groups is associated with the concept of the ontology;

responsive to receiving a confirmation that a word group of the second plurality of word groups represents an object associated with the concept of the ontology, modify a parameter of a classification model that produces a value reflecting a degree of association of a given object with the concept of the ontology.

17 . The computer-readable non-transitory storage medium of claim 16 , wherein executable instructions to identify the second plurality of word groups further comprise executable instructions causing the computing device to:

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

identify a second plurality of semantic structures, each semantic structure of the second plurality of semantic structures representing a sentence comprising at least one word group of the first plurality of word groups;

identify, among the first plurality of semantic structures, a semantic structure that is similar to at least one semantic structure of the second plurality of semantic structures in view of a certain similarity metric; and

identify a word group corresponding to the identified semantic structure as associated with the second plurality of word groups.

18 . The computer-readable non-transitory storage medium of claim 16 , further comprising executable instructions causing the computing device to:

employ the classification model for expanding the ontology.

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

utilize the ontology for performing a natural language processing operation.