Natural language text classification based on semantic features

1. A method, comprising:

performing, by a computer system, semantico-syntactic analysis of a natural language text to produce a semantic structure representing a set of semantic classes;

associating a first semantic class of the set of semantic classes with a first value reflecting a specified semantic class attribute;

identifying a second semantic class associated with the first semantic class by a pre-defined semantic relationship, wherein an instance of the second semantic class is an ancestor of the first semantic class in a semantic hierarchy associated with the set of semantic classes;

associating the second semantic class with a second value reflecting the specified semantic class attribute, wherein the second value is determined by applying a pre-defined transformation to the first value;

evaluating a feature of the natural language text based on the first value and the second value;

determining, by a classifier model using the evaluated feature of the natural language text, a degree of association of the natural language text with a particular category of a pre-defined set of categories; and

performing, using the degree of association, a natural language processing operation.

2. The method of claim 1 , wherein the feature of the natural language text reflects a frequency of occurrence of instances of the first semantic class within the semantic structure.

3. The method of claim 1 , wherein the semantic structure is represented by a graph comprising a plurality of nodes corresponding to semantic classes and further comprising a plurality of edges corresponding to semantic relationships.

4. The method of claim 1 wherein the specified attribute of the first semantic class comprises at least one of: a lexical attribute, a semantic attribute, or a syntactic attribute.

5. The method of claim 1 , wherein applying the pre-defined transformation comprises multiplying the first value by a pre-defined multiplier.

6. The method of claim 5 , further comprising:

sequentially applying, to a plurality of attributes of a chain of related semantic classes, the pre-defined transformation using multipliers that form a geometric sequence of real numbers.

7. The method of claim 1 , further comprising:

identifying a third semantic class associated with the second semantic class by the pre-defined semantic relationship; and

associating the third semantic class with a third value reflecting the specified attribute of the third semantic class, wherein the third value is determined by applying the pre-defined transformation to the second value.

8. A system, comprising:

a memory;

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

perform semantico-syntactic analysis of a natural language text to produce a semantic structure representing a set of semantic classes;

associate a first semantic class of the set of semantic classes with a first value reflecting a specified semantic class attribute;

identify a second semantic class associated with the first semantic class by a pre-defined semantic relationship;

associate the second semantic class with a second value reflecting the specified semantic class attribute, wherein the second value is determined by applying a pre-defined transformation to the first value, wherein applying the pre-defined transformation comprises multiplying the first value by a pre-defined multiplier;

evaluate a feature of the natural language text based on the first value and the second value;

determine, by a classifier model using the evaluated feature of the natural language text, a degree of association of the natural language text with a particular category of a pre-defined set of categories; and

perform, using the degree of association, a natural language processing operation.

9. The system of claim 8 , wherein the feature of the natural language text reflects a frequency of occurrence of instances of the first semantic class within the semantic structure.

10. The system of claim 8 , wherein the semantic structure is represented by a graph comprising a plurality of nodes corresponding to the set of semantic classes and further comprising a plurality of edges corresponding to a plurality of semantic relationships.

11. The system of claim 8 wherein the specified attribute of the first semantic class comprises at least one of: a lexical attribute, a semantic attribute, or a syntactic attribute.

12. The system of claim 8 , wherein an instance of the second semantic class is an ancestor of the first semantic class in a semantic hierarchy associated with the set of semantic classes.

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

perform semantico-syntactic analysis of a natural language text to produce a semantic structure representing a set of semantic classes;

associate a first semantic class of the set of semantic classes with a first value reflecting a specified semantic class attribute;

identify a second semantic class associated with the first semantic class by a pre-defined semantic relationship;

associate the second semantic class with a second value reflecting the specified semantic class attribute, wherein the second value is determined by applying a pre-defined transformation to the first value;

identify a third semantic class associated with the second semantic class by the pre-defined semantic relationship; and

associate the third semantic class with a third value reflecting the specified semantic class attribute, wherein the third value is determined by applying the pre-defined transformation to the second value;

evaluate a feature of the natural language text based on the first value and the second value;

determine, by a classifier model using the evaluated feature of the natural language text, a degree of association of the natural language text with a particular category of a pre-defined set of categories; and

perform, using the degree of association, a natural language processing operation.

14. The computer-readable non-transitory storage medium of claim 13 , wherein the feature of the natural language text reflects a frequency of occurrence of instances of the first semantic class within the semantic structure.

15. The computer-readable non-transitory storage medium of claim 13 , wherein the semantic structure is represented by a graph comprising a plurality of nodes corresponding to the set of semantic classes and further comprising a plurality of edges corresponding to a plurality of semantic relationships.

16. The computer-readable non-transitory storage medium of claim 13 wherein the specified attribute of the first semantic class comprises at least one of: a lexical attribute, a semantic attribute, or a syntactic attribute.

17. The computer-readable non-transitory storage medium of claim 13 , wherein an instance of the second semantic class is an ancestor of the first semantic class in a semantic hierarchy associated with the set of semantic classes.

18. The computer-readable non-transitory storage medium of claim 13 , wherein applying the pre-defined transformation comprises multiplying the first value by a pre-defined multiplier.