Method for handling requests for information in a natural language understanding system

• US 20020103837A1
• Filed: 01/31/2001
• Published: 08/01/2002
• Est. Priority Date: 01/31/2001
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Balchandran, Rajesh, Epstein, Mark E.
• Application Number:
  US09/773,157
• Publication Number:
  US 20020103837A1
• Time in Patent Office: Days
• US Class Current: 715/534
• CPC Class Codes: G06F 40/253 Grammatical analysis; Style critique
• CPC Class Codes: G06F 40/284 Lexical analysis, e.g. tokenisation or collocates
• CPC Class Codes: G06F 40/30 Semantic analysis

Abstract

A multi-pass method for processing text for use with a natural language understanding system can include a series of steps. The steps can include determining at least one contextual marker in the text and identifying a referrent in a question in the text. In a separate referrent mapping pass through the text, the method can include classifying the identified referrent as a particular type of referrent using the contextual marker and the identified referrent.

Claims

1. In a natural language understanding system, a multi-pass method for processing text comprising the steps of:

determining at least one contextual marker in said text;

identifying a referrent in a question in said text; and

in a separate referrent mapping pass through said text, classifying said identified referrent as a particular type of referrent using said contextual marker and said identified referrent.

2. The method of claim 1, wherein said contextual marker is an indicator of whether said question corresponds to an old transaction, a new transaction, or an ongoing transaction.

3. The method of claim 1, wherein said contextual marker is an indicator of the tense of said question.

4. The method of claim 1, wherein said contextual marker is a grammatical part of speech, said part of speech comprising a subject, a verb, or an object of said verb.

5. The method of claim 1, wherein said contextual marker is an indicator of an action.

6. The method of claim 1, wherein said contextual marker is a parameter of an identified action.

7. The method of claim 1, further comprising the step of:

in said referrent mapping pass, providing a probability distribution over all possible types of referrents.

8. The method of claim 1, said classifying step classifying each said identified referrent as one or more particular types of referrent.

9. The method of claim 8, wherein said particular types of referrents have been identified as having a probability at least equal to a predetermined threshold probability value.

10. The method of claim 1, wherein said classifying step is performed using a lookup table of possible referrent types.

11. The method of claim 1, wherein said classifying step is performed using maximum entropy statistical processing.

12. The method of claim 1, wherein said classifying step is performed using regular expression matching.

13. The method of claim 1, wherein said classifying step is performed using ordered rules.

14. The method of claim 1, wherein said classifying step is performed using statistical parsing.

16. The machine readable storage of claim 15, wherein said contextual marker is an indicator of whether said question corresponds to an old transaction, a new transaction, or an ongoing transaction.

17. The machine readable storage of claim 15, wherein said contextual marker is an indicator of the tense of said question.

18. The machine readable storage of claim 15, wherein said contextual marker is a grammatical part of speech, said part of speech comprising a subject, a verb, or an object of said verb.

19. The machine readable storage of claim 15, wherein said contextual marker is an indicator of an action.

20. The machine readable storage of claim 15, wherein said contextual marker is a parameter of an identified action.

21. The machine readable storage of claim 15, further comprising the step of:

in said referrent mapping pass, providing a probability distribution over all possible types of referrents.

22. The machine readable storage of claim 15, said classifying step classifying each said identified referrent as one or more particular types of referrent.

23. The machine readable storage of claim 22, wherein said particular types of referrents have been identified as having a probability at least equal to a predetermined threshold probability value.

24. The machine readable storage of claim 15, wherein said classifying step is performed using a lookup table of possible referrent types.

25. The machine readable storage of claim 15, wherein said classifying step is performed using maximum entropy statistical processing.

26. The machine readable storage of claim 15, wherein said classifying step is performed using regular expression matching.

27. The machine readable storage of claim 15, wherein said classifying step is performed using ordered rules.

28. The machine readable storage of claim 15, wherein said classifying step is performed using statistical processing.

15. A machine readable storage, having stored thereon a computer program having a plurality of code sections executable by a machine for causing the machine to perform the steps of:

determining at least one contextual marker in said text;

identifying a referrent in a question in said text; and

in a separate referrent mapping pass through said text, classifying said identified referrent as a particular type of referrent using said contextual marker and said identified referrent.