System and method for coupled detection of syntax and semantics for natural language understanding and generation

1. A method comprising:

constructing, by at least one processing device, content semantics from closest nodes in a syntax tree to a clause, each closest node having a property that a child of the node is not as common relative to the node as the node is to its parent; and

performing, by the at least one processing device, natural language understanding using both (i) a semantic word and clause representation generated from syntactically-labeled context, the semantic word and clause representation comprising the content semantics, and (ii) a syntax generated from common semantic relations between sequential words and clauses.

2. The method of claim 1 , wherein the semantic word and clause representation and the syntax are generated iteratively.

3. The method of claim 1 , further comprising:

generating the semantic word and clause representation, wherein a significance of particular items of context is modified by a presence of words or clauses from a given lexicon of amplifiers along a syntax tree.

4. The method of claim 1 , further comprising:

generating the semantic word and clause representation by performing disambiguation to separate instances of particular items of context that have different semantic meanings, wherein performing the disambiguation comprises clustering instances of words and clauses based on similarity of their syntactically-labeled context.

5. The method of claim 1 , further comprising:

generating the syntax, wherein multiple semantic relations are considered to be equivalent if the multiple semantic relations have identical representations in an auto-associative memory.

6. The method of claim 1 , further comprising:

measuring a sentiment of a word or clause based on components of the semantic word and clause representation coming from emotional context according to a given lexicon of emotional words and clauses.

7. The method of claim 1 , further comprising:

performing, with the at least one processing device, natural language generation using the semantic word and clause representation and the syntax.

8. A method comprising:

performing, with at least one processing device, natural language understanding using both (i) a semantic word and clause representation generated from syntactically-labeled context and (ii) a syntax generated from common semantic relations between sequential words and clauses; and

determining properties of words and clauses by calculating inner products of semantic vectors and semantic co-vectors, the semantic vectors and semantic co-vectors comprising coefficients corresponding to context items, the semantic vectors comprising sparse vectors, the semantic co-vectors comprising patterned vectors with coefficient values that are repeated across context items sharing a semantic property.

9. An apparatus comprising:

at least one processing device; and

at least one memory storing instructions that, when executed by the at least one processing device, cause the at least one processing device to:

construct content semantics from closest nodes in a syntax tree to a clause, each closest node having a property that a child of the node is not as common relative to the node as the node is to its parent; and

perform natural language understanding using both (i) a semantic word and clause representation generated from syntactically-labeled context, the semantic word and clause representation comprising the content semantics, and (ii) a syntax generated from common semantic relations between sequential words and clauses.

10. The apparatus of claim 9 , wherein the semantic word and clause representation and the syntax are generated iteratively.

11. The apparatus of claim 9 , wherein:

the at least one memory further stores instructions that, when executed by the at least one processing device, cause the at least one processing device to generate the semantic word and clause representation; and

a significance of particular items of context is modified by a presence of words or clauses from a given lexicon of amplifiers along a syntax tree.

12. The apparatus of claim 9 , wherein:

the at least one memory further stores instructions that, when executed by the at least one processing device, cause the at least one processing device to generate the semantic word and clause representation by performing disambiguation to separate instances of particular items of context that have different semantic meanings; and

the instructions that when executed cause the at least one processing device to perform the disambiguation comprise instructions that when executed cause the at least one processing device to cluster instances of words and clauses based on similarity of their syntactically-labeled context.

13. The apparatus of claim 9 , wherein:

the at least one memory further stores instructions that, when executed by the at least one processing device, cause the at least one processing device to generate the syntax; and

multiple semantic relations are considered to be equivalent if the multiple semantic relations have identical representations in an auto-associative memory.

14. The apparatus of claim 9 , wherein the at least one memory further stores instructions that, when executed by the at least one processing device, cause the at least one processing device to measure a sentiment of a word or clause based on components of the semantic word and clause representation coming from emotional context according to a given lexicon of emotional words and clauses.

15. The apparatus of claim 9 , wherein the at least one memory further stores instructions that, when executed by the at least one processing device, cause the at least one processing device to perform natural language generation using the semantic word and clause representation and the syntax.

16. An apparatus comprising:

at least one processing device; and

at least one memory storing instructions that, when executed by the at least one processing device, cause the at least one processing device to:

perform natural language understanding using both (i) a semantic word and clause representation generated from syntactically-labeled context and (ii) a syntax generated from common semantic relations between sequential words and clauses; and

determine properties of words and clauses by calculating inner products of semantic vectors and semantic co-vectors, the semantic vectors and semantic co-vectors comprising coefficients corresponding to context items, the semantic vectors comprising sparse vectors, the semantic co-vectors comprising patterned vectors with coefficient values that are repeated across context items sharing a semantic property.

17. A non-transitory computer readable medium containing instructions that, when executed by at least one processing device, cause the at least one processing device to:

construct content semantics from closest nodes in a syntax tree to a clause, each closest node having a property that a child of the node is not as common relative to the node as the node is to its parent; and

perform natural language understanding using both (i) a semantic word and clause representation generated from syntactically-labeled context, the semantic word and clause representation comprising the content semantics, and (ii) a syntax generated from common semantic relations between sequential words and clauses.

18. The non-transitory computer readable medium of claim 17 , wherein the semantic word and clause representation and the syntax are generated iteratively.

19. The non-transitory computer readable medium of claim 17 , further containing instructions that, when executed by the at least one processing device, cause the at least one processing device to:

generate the semantic word and clause representation, wherein a significance of particular items of context is modified by a presence of words or clauses from a given lexicon of amplifiers along a syntax tree.

20. The non-transitory computer readable medium of claim 17 , further containing instructions that, when executed by the at least one processing device, cause the at least one processing device to generate the semantic word and clause representation by performing disambiguation to separate instances of particular items of context that have different semantic meanings; and

the instructions that when executed cause the at least one processing device to perform the disambiguation comprise instructions that when executed cause the at least one processing device to cluster instances of words and clauses based on similarity of their syntactically-labeled context.

21. The non-transitory computer readable medium of claim 17 , further containing instructions that, when executed by the at least one processing device, cause the at least one processing device to:

generate the syntax, wherein multiple semantic relations are considered to be equivalent if the multiple semantic relations have identical representations in an auto-associative memory.

22. The non-transitory computer readable medium of claim 17 , further containing instructions that, when executed by the at least one processing device, cause the at least one processing device to:

measure a sentiment of a word or clause based on components of the semantic word and clause representation coming from emotional context according to a given lexicon of emotional words and clauses.

23. The non-transitory computer readable medium of claim 17 , further containing instructions that, when executed by the at least one processing device, cause the at least one processing device to perform natural language generation using the semantic word and clause representation and the syntax.

24. A non-transitory computer readable medium containing instructions that, when executed by at least one processing device, cause the at least one processing device to:

perform natural language understanding using both (i) a semantic word and clause representation generated from syntactically-labeled context and (ii) a syntax generated from common semantic relations between sequential words and clauses; and

determine properties of words and clauses by calculating inner products of semantic vectors and semantic co-vectors, the semantic vectors and semantic co-vectors comprising coefficients corresponding to context items, the semantic vectors comprising sparse vectors, the semantic co-vectors comprising patterned vectors with coefficient values that are repeated across context items sharing a semantic property.