SEARCH QUERY DISAMBIGUATION

• US 20100205198A1
• Filed: 02/06/2009
• Published: 08/12/2010
• Est. Priority Date: 02/06/2009
• Status: Abandoned Application

• Current Assignee: Oath Inc. (Verizon Communications Inc.)
• Original Assignee: Oath Inc. (Verizon Communications Inc.)
• Inventors: Stata, Raymond, Peng, Fuchun, Mishne, Gilad
• Application Number:
  US12/367,114
• Publication Number:
  US 20100205198A1
• Time in Patent Office: Days
• US Class Current: 707/759
• CPC Class Codes: G06F 16/3346 using probabilistic model
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques

Abstract

Disclosed herein is a system and method of query disambiguation. At least one model is generated using training data, which model can be used to score, or rank, possible interpretations identified for a query, which can be used to select an interpretation from a number of possible interpretations. A selected interpretation can be used to process a web search request, e.g., to generate search results that relate to the selected query interpretation, rank or order the items in the search result based on relevance to the selected query interpretation, and/or identify a presentation to be used to display the search results based on the selected query interpretation.

Claims

1. A method comprising:

receiving a query in a web search request;

identifying a plurality of interpretations of the received query, each interpretation comprising at least one confidence score;

selecting at least one of the plurality of interpretations of the query for use in a web search, said selecting using the at least one confidence score of each interpretation of the plurality.

2. The method of claim 1, further comprising generating the at least one confidence score for each identified interpretation.

3. The method of claim 2, said generating the at least one confidence score for each identified interpretation further comprising:

determining a plurality of features for the interpretation; and

scoring the interpretation using the plurality of features determined for the interpretation to generate the at least one confidence score for the interpretation.

4. The method of claim 3, wherein a feature of the plurality is one of a span-level feature, an interpretation-level feature and a query-level feature.

5. The method of claim 3, said scoring being performed using a model, the method further comprising training the model.

6. The method of claim 5, said training the model further comprising:

obtaining training data comprising a plurality of query interpretations, each query interpretation having an associated confidence score and comprising at least one entity and entity type pair;

determining a plurality of features for each interpretation; and

training the model using the training data and the plurality of features.

7. The method of claim 6, said obtaining training data further comprising:

receiving input from a plurality of editors, the input identifying at least one interpretation for each of a plurality of queries and at least one score for each interpretation.

8. The method of claim 6, said obtaining training data further comprising:

generating at least a portion of the training data.

9. The method of claim 8, said generating at least a portion of the training data further comprising:

generating a plurality of interpretations from a plurality of queries and search results for the plurality of queries.

10. The method of claim 1, wherein the received query comprises at least one term and each interpretation comprises at least one entity and entity type pair, each entity comprising one or more terms of the query and each entity type comprising information identifying an entity category.

11. The method of claim 10, wherein the at least one confidence score comprises a score for the at least one entity and entity type pair.

12. The method of claim 1, wherein the at least one confidence score for an interpretation is a ranking relative to at least one other interpretation.

13. The method of claim 1, wherein the query comprises a numeric term, the method further comprising:

identifying an ambiguity associated with the numeric term; and

modifying the query to address the ambiguity.

26. The system of claim 1, wherein the query comprises a numeric term, said at least one server further configured to:

identify an ambiguity associated with the numeric term; and

modify the query to address the ambiguity.

14. A system comprising:

at least one server configured to;

receive a query in a web search request;

identify a plurality of interpretations of the received query, each interpretation comprising at least one confidence score;

select at least one of the plurality of interpretations of the query for use in a web search, said selecting using the at least one confidence score of each interpretation of the plurality.

15. The system of claim 14, said at least one server further configured to generate the at least one confidence score for each identified interpretation.

16. The system of claim 15, said at least one server configured to generate the at least one confidence score for each identified interpretation further configured to:

determine a plurality of features for the interpretation; and

score the interpretation using the plurality of features determined for the interpretation to generate the at least one confidence score for the interpretation.

17. The system of claim 16, wherein a feature of the plurality is one of a span-level feature, an interpretation-level feature and a query-level feature.

18. The system of claim 16, wherein the interpretation is scored using a model, said at least one server further configured to train the model.

19. The system of claim 18, said at least one server configured to train the model further configured to:

obtain training data comprising a plurality of query interpretations, each query interpretation having an associated confidence score and comprising at least one entity and entity type pair;

determine a plurality of features for each interpretation; and

train the model using the training data and the plurality of features.

20. The system of claim 19, said at least one server configured to obtain training data further configured to:

receive input from a plurality of editors, the input identifying at least one interpretation for each of a plurality of queries and at least one score for each interpretation.

21. The system of claim 19, said at least one server configured to obtain training data further configured to:

generate at least a portion of the training data.

22. The system of claim 21, said at least one server configured to generate at least a portion of the training data further configured to:

generate a plurality of interpretations from a plurality of queries and search results for the plurality of queries.

23. The system of claim 14, wherein the received query comprises at least one term and each interpretation comprises at least one entity and entity type pair, each entity comprising one or more terms of the query and each entity type comprising information identifying an entity category.

24. The system of claim 23, wherein the at least one confidence score comprises a score for the at least one entity and entity type pair.

25. The system of claim 14, wherein the at least one confidence score for an interpretation is a ranking relative to at least one other interpretation.

27. A computer-readable medium tangibly storing program code, the program code comprising:

code to receive a query in a web search request;

code to identify a plurality of interpretations of the received query, each interpretation comprising at least one confidence score;

code to select at least one of the plurality of interpretations of the query for use in a web search, said selecting using the at least one confidence score of each interpretation of the plurality.

28. The medium of claim 27, the program code further comprising code to generate the at least one confidence score for each identified interpretation.

29. The medium of claim 28, the code to generate the at least one confidence score for each identified interpretation further comprising:

code to determine a plurality of features for the interpretation; and

code to score the interpretation using the plurality of features determined for the interpretation to generate the at least one confidence score for the interpretation.

30. The medium of claim 29, wherein a feature of the plurality is one of a span-level feature, an interpretation-level feature and a query-level feature.

31. The medium of claim 29, the code to score using a model to score the interpretation, the program code further comprising code to train the model.

32. The medium of claim 31, the code to train the model further comprising:

code to obtain training data comprising a plurality of query interpretations, each query interpretation having an associated confidence score and comprising at least one entity and entity type pair;

code to determine a plurality of features for each interpretation; and

code to train the model using the training data and the plurality of features.

33. The medium of claim 32, the code to obtain training data further comprising:

code to receive input from a plurality of editors, the input identifying at least one interpretation for each of a plurality of queries and at least one score for each interpretation.

34. The medium of claim 32, the code to obtain training data further comprising:

code to generate at least a portion of the training data.

35. The medium of claim 34, the code to generate at least a portion of the training data further comprising:

code to generate a plurality of interpretations from a plurality of queries and search results for the plurality of queries.

36. The medium of claim 27, wherein the received query comprises at least one term and each interpretation comprises at least one entity and entity type pair, each entity comprising one or more terms of the query and each entity type comprising information identifying an entity category.

37. The medium of claim 36, wherein the at least one confidence score comprises a score for the at least one entity and entity type pair.

38. The medium of claim 27, wherein the at least one confidence score for an interpretation is a ranking relative to at least one other interpretation.

39. The medium of claim 27, wherein the query comprises a numeric term, the program code further comprising:

code to identify an ambiguity associated with the numeric term; and

code to modify the query to address the ambiguity.