DYNAMIC NATURAL LANGUAGE UNDERSTANDING

• US 20080154581A1
• Filed: 03/12/2008
• Published: 06/26/2008
• Est. Priority Date: 03/13/2001
• Status: Abandoned Application

• Current Assignee: Intelligate, Ltd. (AskMeNow, Inc.)
• Original Assignee: Intelligate, Ltd. (AskMeNow, Inc.)
• Inventors: Auerbach, Gadiel, Persky, Eldad, Lavi, Ofer
• Application Number:
  US12/047,003
• Publication Number:
  US 20080154581A1
• Time in Patent Office: Days
• US Class Current: 704/9
• CPC Class Codes: G06F 40/284 Lexical analysis, e.g. tokenisation or collocates
• CPC Class Codes: G06F 40/30 Semantic analysis

Abstract

Methods and systems for dynamic natural language understanding. A hierarchical structure of semantic categories is exploited to assist in the natural language understanding. Optionally, the natural language to be understood includes a request.

Claims

1. A method for use in a method for understanding a natural language text, comprising performing the following selectively in a statistical manner:

attempting to extract at least one value belonging to a semantic category from a natural language text or a form thereof; and

if a result of said attempting complies with a predetermined criterion, attempting to extract, based on said result, at least one value belonging to another semantic category of a different hierarchical level than said semantic category, else performing at least one action from a group of actions including;

asking a submitter of said text a question whose content depends on said result and giving up on understanding said natural language text.

2. The method of claim 1, wherein said attempting to extract at least one value belonging to said another semantic category includes:

selecting at least one classifier or pseudo classifier for said another semantic category from among more than said at least one classifier or pseudo classifier for said another semantic category, wherein said selecting is based on at least one extracted value belonging to said semantic category; and

employing said at least one classifier or pseudo classifier in an attempt to extract at least one value belonging to said another semantic category.

3. The method of claim 2, wherein said another semantic category is a hierarchically lower level semantic category than said semantic category.

4. The method of claim 3, wherein said at least one value belonging to said another semantic category is at least one operation and said at least one value belonging to said semantic category is at least one domain.

5. The method of claim 1, wherein said form are n grams constructed from said text or from a restatement of said text which includes at least one embedded token.

6. The method of claim 5, wherein said n-grams are sparse n-grams.

7. The method of claim 1, wherein said asking a question includes:

formulating said content of said question on the fly based on said result.

8. The method of claim 1, wherein said asking a question includes:

modifying a predefined question based on said result.

9. The method of claim 1, further comprising:

if said question is asked, attempting to extract at least one value belonging to a previously extracted semantic category from said answer.

10. The method of claim 9, wherein said previously extracted semantic category is said semantic category.

11. The method of claim 1, wherein said at least one value belonging to a semantic category and said at least one value belonging to another semantic category are at least one from a group including:

at least one domain, at least one operation, and at least one parameter value.

12. The method of claim 1, wherein said attempting to extract includes employing at least one pseudo classifier in order to attempt to extract at least one parameter value.

13. The method of claim 12, wherein at least one of said pseudo classifiers is a time extractor and said employing includes employing said time extractor in order to attempt to extract at least one time expression.

14. The method of claim 12, wherein at least one of said pseudo classifiers is a currency extractor and said employing includes employing said currency extractor in order to attempt to extract at least one currency expression.

15. The method of claim 12, wherein at least one of said pseudo classifiers is employed on a part of said natural language text or a form thereof.

16. The method of claim 1, wherein said content is based on a comparison between said result and predetermined logic related to said semantic category.

17. The method of claim 16, wherein said logic was devised to assist in understanding said natural language text.

18. The method of claim 1, wherein said question relates to less than all non-extracted values, based on relative importance.

19. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for use in a method for understanding a natural language text, comprising performing the following selectively in a statistical manner:

attempting to extract at least one value belonging to a semantic category from a natural language text or a form thereof; and

if a result of said attempting complies with a predetermined criterion, attempting to extract, based on said result, at least one value belonging to another semantic category of a different hierarchical level than said semantic category, else performing at least one action from a group of actions including;

asking a submitter of said text a question whose content depends on said result and giving up on understanding said natural language text.

20. A computer program product comprising a computer useable medium having computer readable program code embodied therein for use in a computer program product comprising:

computer readable program code for causing the computer to perform the following selectively in a statistical manner;

computer readable program code for causing the computer to attempt to extract at least one value belonging to a semantic category from a natural language text or a form thereof; and

computer readable program code for causing the computer, if a result of said attempting complies with a predetermined criterion, to attempt to extract, based on said result, at least one value belonging to another semantic category of a different hierarchical level than said semantic category, else to perform at least one action from a group of actions including;

asking a submitter of said text a question whose content depends on said result and giving up on understanding said natural language text.