Natural language question answering system and method utilizing multi-modal logic

• US 20060053000A1
• Filed: 10/07/2005
• Published: 03/09/2006
• Est. Priority Date: 05/11/2004
• Status: Abandoned Application

• Current Assignee: Lymba Corp.
• Original Assignee: Lymba Corp.
• Inventors: Tatu, Marta, Moldovan, Dan I.
• Application Number:
  US11/246,621
• Publication Number:
  US 20060053000A1
• Time in Patent Office: Days
• US Class Current: 704/9
• CPC Class Codes: G06F 16/243 Natural language query formulation
• CPC Class Codes: G06F 40/30 Semantic analysis

Abstract

A multi-modal natural language question answering system and method comprises receiving a question logic form, at least one answer logic form, and utilizing semantic relations, contextual information, and adaptable logic.

Claims

1. A method for natural language question answering, comprising:

receiving a question logic form, at least one answer logic form, and extended lexical information by a first module;

outputting at least one contextual index to a second module; and

utilizing the contextual index by the second module to provide an answer.

2. The method of claim 1, comprising outputting at least one answer based on at least one previously ranked candidate answer associated with at least one of:

the question logic form, the answer logic form, and the contextual index.

3. The method of claim 2, wherein the outputted answer includes at least one of:

an exact answer, a phrase answer, a sentence answer, a multi-sentence answer.

4. The method of claim 3, comprising re-ranking the outputted answer based on the previously ranked candidate answer.

5. The method of claim 1, comprising outputting at least one answer justification based on at least one candidate answer associated with at least one of:

the question logic form including at least one contextual index and the answer logic form including at least ones contextual index.

6. The method of claim 5, wherein the outputted answer justification includes at least one of:

every contextual index used, question terms that unify with answer terms, predicate arguments dropped, predicates dropped, and answer extraction.

7. The method of claim 1, wherein the question logic form is related to the answer logic form.

8. The method of claim 1, wherein the utilized contextual index are at least one of a following index from a group consisting of:

Subjective context;

Beliefs context;

Fictive context;

Planning context;

Volitional context;

Probability, possibility, uncertainty, likelihood context;

Temporal context;

Spatial context;

Domain context; and

Conditional context.

9. The method of claim 1, wherein the contextual index are of a type are at least one of a following type from a group consisting of:

Subjective context;

Beliefs context;

Fictive context;

Planning context;

Volitional context;

Probability, possibility, uncertainty, likelihood context;

Temporal context;

Spatial context;

Domain context; and

Conditional context.

10. The method of claim 9, wherein the subjective type of contextual index is selected from the group of:

statements, beliefs, fictive, planning and volitional.

18. The method of claim 1, wherein the question logic form is based on natural language.

19. The method of claim 1, wherein the answer logic form is based on natural language.

11. A method for natural language question answering, comprising:

receiving a question logic form, at least one answer logic form, and extended lexical information by a first module;

outputting at least one semantic relation to a second module; and

utilizing the semantic relation by the second module to provide an answer.

12. The method of claim 11, wherein further comprising the step of outputting a combination of semantic relations.

13. The method of claim 12, further comprising the step of utilizing the combination of semantic relations to provide the answer.

14. The method of claim 11, wherein the semantic relation is selected from the group comprising:

Possession;

Instrument;

Associated-With/Other;

Kinship;

Location-Space;

Measure;

Property-Attribute Holder;

Purpose;

Synonymy-Name;

Agent;

Source-From;

Antonymy;

Temporal;

Topic;

Probability;

Depiction;

Manner;

Possibility;

Part-Whole;

Means;

Certainty;

Hyponymy;

Accompaniment-Companion;

Theme-Patient;

Entail;

Experiencer;

Result;

Cause;

Recipient;

Stimulus;

Make-Produce;

Frequency;

Extent;

Influence;

Predicate;

Causality;

Goal;

Justification;

Meaning; and

Belief.

15. The method of claim 14, wherein the semantic operation is selected from the group comprising:

reverse;

composition;

dominance;

union;

intersection; and

difference.

16. The method of claim 14, further comprising a semantic operation and two or more semantic relations to generate a semantic axiom.

17. The method of claim 16, wherein parsing operation is selected from the group comprising:

reverse;

composition;

dominance; and

union.

20. The method of claim 11, comprising outputting at least one said answer based on at least one previously ranked candidate answer associated with at least one of:

the question logic form including at least one semantic relation and the answer logic form including at least one semantic relation.

21. A method for natural language question answering, comprising:

receiving a question logic form, at least one answer logic form, and extended lexical information by a first module; and

adapting an inference mechanism and logic to provide an answer.

22. The method of claim 21, wherein the logic is first order logic.

23. The method of claim 21, wherein the logic is non-monotonic logic including default reasoning.

24. The method of claim 21, wherein the logic is description logic.

25. The method of claim 21, wherein the question logic form is based on natural language.

26. The method of claim 21, wherein the answer logic form is based on natural language.

27. A method for natural language question answering, comprising:

receiving a question logic form, at least one answer logic form, and extended lexical information by a first module; and

utilizing multi-modal logic to provide an answer.

28. The method of claim 27, wherein the multi-modal logic is based on at least one selected from the group consisting of:

semantic combination axioms;

contextual index information;

inference mechanism selector utilizing at least one logic selected from the group comprising;

first order logic;

non-monotonic logic, and

description logic.

29. The method of claim 28, wherein the modal logic is selected as a function of the question logic form.

30. The method of claim 29, wherein the modal logic is selected as a function of the answer logic form.

31. The method of claim 30, further comprising performing justification within the selected logic mode between the question logic form and the answer logic form using axioms.

32. The method of claim 31, wherein the used axioms are weighted semantic axioms.

33. The method of claim 27, wherein the question logic form and the answer logic form are based on natural language.

34. A natural language question answering system, comprising;

a first module configured to receive a question logic form, at least one answer logic form, and extended lexical information; and

a second module responsive to the first module and having a contextual index and configured to output an answer as a function of the question logic form and the contextual index.

35. A natural language question answering system, comprising;

a first module configured to receive a question logic form, at least one answer logic form, and extended lexical information; and

a second module responsive to the first module and configured to utilize a semantic relation of the question logic form to output an answer.

36. A natural language question answering system, comprising;

a first module configured to receive a question logic form, at least one answer logic form, and extended lexical information; and

a second module responsive to the first module and configured to utilize an inference mechanism and logic to provide an answer.

37. A natural language question answering system, comprising;

a first module configured to receive a question logic form, at least one answer logic form, and extended lexical information; and

a second module responsive to the first module and configured to utilize multi-modal logic to provide an answer.

38. A computer readable medium including instructions for:

receiving a question logic form, at least one answer logic form, and extended lexical information by a first module;

outputting at least one contextual index to a second module; and

utilizing the contextual index by the second module to provide an answer.

39. A computer readable medium including instructions for:

receiving a question logic form, at least one answer logic form, and extended lexical information by a first module;

outputting at least one semantic relation to a second module; and

utilizing the semantic relation by the second module to provide an answer.

40. A computer readable medium including instructions for:

receiving a question logic form, at least one answer logic form, and extended lexical information by a first module; and

adapting an inference mechanism and logic to provide an answer.

41. A computer readable medium including instructions for:

receiving a question logic form, at least one answer logic form, and extended lexical information by a first module; and

utilizing multi-modal logic to provide an answer.