Method for personalized named entity recognition

• US 20080052262A1
• Filed: 08/22/2006
• Published: 02/28/2008
• Est. Priority Date: 08/22/2006
• Status: Abandoned Application

• Current Assignee: Carole Dulong, Igor Kozintsev, Marzia Polito, Serhiy Kosinov
• Original Assignee: Carole Dulong, Igor Kozintsev, Marzia Polito, Serhiy Kosinov
• Inventors: Kosinov, Serhiy, Kozintsev, Igor, Polito, Marzia, Dulong, Carole
• Application Number:
  US11/508,579
• Publication Number:
  US 20080052262A1
• Time in Patent Office: Days
• US Class Current: 707/1
• CPC Class Codes: G06F 16/50 of still image data
• CPC Class Codes: G06F 16/9032 Query formulation
• CPC Class Codes: G06F 16/90335 Query processing

Abstract

Personalized named entity recognition may be accomplished by parsing input text to determine a subset of the input text, generating a plurality of queries based at least in part on the subset of the input text, submitting the queries to a plurality of reference resources, processing responses to the queries and generating a vector based on the responses, and performing classification based at least in part on the vector and a set of model parameters to determine a likelihood as to which named entity category the input text belongs.

Claims

1. A method of personalized named entity recognition comprising:

parsing input text to determine a subset of the input text;

generating a plurality of queries based at least in part on the subset of the input text;

submitting the queries to a plurality of reference resources;

processing responses to the queries and generating a vector based on the responses; and

performing classification based at least in part on the vector and a set of model parameters to determine a likelihood as to which named entity category the input text belongs.

2. The method of claim 1, wherein the subset comprises a head noun of the input text.

3. The method of claim 1, wherein at least one of the reference resources comprises an on-line web site.

4. The method of claim 1, wherein at least one of the reference resources comprises an offline application program.

5. The method of claim 1, wherein the vector comprises a plurality of numeric values, each numeric value representing the likelihood that the subset of the input text corresponds to a term in a term vocabulary data structure.

6. The method of claim 1, wherein the classification performed comprises support vector machine-based classification.

7. The method of claim 1, further comprising accepting user feedback to update the set of model parameters.

8. The method of claim 1, wherein the named entity categories in a named entity hierarchy comprise at least people names, place names, and event names, the named entity hierarchy being extendable to other categories.

9. The method of claim 3, wherein the reference resources comprise one or more dictionaries, directories, semantic lexicons, and gazetteers, and the responses from the reference resources are represented as numeric values in the vector.

10. The method of claim 1, wherein parsing is performed independent of context of the input text.

11. The method of claim 5, wherein processing responses to the queries comprises combining a character-level inexact similarity model with exact lexical matching to determine the numeric value stored in the vector for a query.

12. The method of claim 1, wherein the input text comprises one of at least a portion of a filename of a multimedia file and a tag associated with the multimedia file.

13. An article comprising:

a tangible machine accessible medium containing instructions, which when executed, result in personalized named entity recognition by

parsing input text to determine a subset of the input text;

generating a plurality of queries based at least in part on the subset of the input text;

submitting the queries to a plurality of reference resources;

processing responses to the queries and generating a vector based on the responses; and

performing classification based at least in part on the vector and a set of model parameters to determine a likelihood as to which named entity category the input text belongs.

14. The article of claim 13, wherein the vector comprises a plurality of numeric values, each numeric value representing the likelihood that the subset of the input text corresponds to a term in a term vocabulary data structure.

15. The article of claim 13, further comprising instructions to accept user feedback to update the set of model parameters.

16. The article of claim 13, wherein the named entity categories in a named entity hierarchy comprise at least people names, place names, and event names, the named entity hierarchy being extendable to other categories.

17. The article of claim 13, wherein the reference resources comprise one or more of dictionaries, directories, semantic lexicons, and gazetteers, and the responses from the reference resources are represented as numeric values in the vector.

18. The article of claim 13, wherein parsing the input text is performed independent of context of the input text.

19. The article of claim 13, wherein processing responses to the queries comprises combining a character-level inexact similarity model with exact lexical matching to determine the numeric value stored in the vector for a query.

20. A personalized named entity recognition system comprising:

a parser module to parse input text to determine a subset of the input text;

a query generation module to generate a plurality of queries based at least in part on the subset of the input text, and to submit the queries to a plurality of reference resources;

a response processing module to process responses to the queries and generating a vector based on the responses;

a classifier to perform classification based at least in part on the vector and a set of model parameters; and

a category decision module to determine a likelihood as to which named entity category the input text belongs based at least in part on the classification.

21. The personalized named entity recognition system of claim 20, further comprising a user feedback module to update the set of model parameters during classifier training.

22. The personalized named entity recognition system of claim 20, wherein the subset comprises a head noun of the input text.

23. The personalized named entity recognition system of claim 20, wherein the vector comprises a plurality of numeric values, each numeric value representing the likelihood that the subset of the input text corresponds to a term in a term vocabulary data structure.

24. The personalized named entity recognition system of claim 20, wherein the classification module comprises a support vector machine-based classifier.

25. The personalized named entity recognition system of claim 20, wherein the named entity categories in a named entity hierarchy comprise at least people names, place names, and event names, the named entity hierarchy being extendable to other categories.

26. The personalized named entity recognition system of claim 20, wherein the reference resources comprise a plurality of at least one of online and offline resources, including one or more of dictionaries, directories, semantic lexicons, and gazetteers, and the responses from the reference resources are represented as numeric values in the vector.

27. The personalized named entity recognition system of claim 20, wherein the parsing is performed independent of context of the input text.

28. The personalized named entity recognition system of claim 20, wherein the response processing module is adapted to combine a character-level inexact similarity model with exact lexical matching to determine the numeric value stored in the vector for a query.

29. The personalized named entity recognition system of claim 20, wherein the input text comprise one of at least a portion of a filename of a multimedia file and a tag associated with the multimedia file.

30. A system comprising:

a multimedia database to store a plurality of multimedia files;

a personal multimedia application to access the multimedia files; and

a named entity recognition system coupled to the personal multimedia application, the named entity recognition system comprising

a parser module to parse input text to determine a subset of the input text;

a query generation module to generate a plurality of queries based at least in part on the subset of the input text, and to submit the queries to a plurality of reference resources;

a response processing module to process responses to the queries and generating a vector based on the responses;

a classifier to perform classification based at least in part on the vector and a set of model parameters; and

a category decision module to determine a likelihood as to which named entity category the input text belongs based at least in part on the classification.

31. The system of claim 30, wherein the personal multimedia application is adapted to search for one or more multimedia files in the multimedia database based at least in part on the named entity category determined by the category decision module.

32. The system of claim 30, wherein the reference resources comprise one or more dictionaries, directories, semantic lexicons, and gazetteers, and the responses from the reference resources are represented as numeric values in the vector.

33. The system of claim 30, wherein the parser module is adapted to parse the input text independent of context of the input text.

34. The system of claim 30, wherein the response processing module is adapted to combine a character-level inexact similarity model with exact lexical matching to determine the numeric value stored in the vector for a query.

35. The system of claim 30, wherein the input text comprises one of at least a portion of a filename of a multimedia file and a tag associated with the multimedia file.

36. The system of claim 30, wherein the named entity categories in a named entity hierarchy comprise at least people names, place names, and event names, the named entity hierarchy being extendable to other categories.