MINING PHRASE PAIRS FROM AN UNSTRUCTURED RESOURCE

• US 20100299132A1
• Filed: 05/22/2009
• Published: 11/25/2010
• Est. Priority Date: 05/22/2009
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Brockett, Christopher J., Dolan, William B., Castillo, Julio J., Vanderwende, Lucretia H.
• Application Number:
  US12/470,492
• Publication Number:
  US 20100299132A1
• Time in Patent Office: Days
• US Class Current: 704/2
• CPC Class Codes: G06F 40/44 Statistical methods, e.g. probability models
• CPC Class Codes: G06F 40/49 using very large corpora, e.g. the web

Abstract

A mining system applies queries to retrieve result items from an unstructured resource. The unstructured resource may correspond to a repository of network-accessible resource items. The result items that are retrieved may correspond to text segments (e.g., sentence fragments) associated with resource items. The mining system produces a structured training set by filtering the result items and establishing respective pairs of result items. A training system can use the training set to produce a statistical translation model. The translation model can be used in a monolingual context to translate between semantically-related phrases in a single language. The translation model can also be used in a bilingual context to translate between phrases expressed in two respective languages. Various applications of the translation model are also described.

Claims

1. A method, using electrical data processing functionality, for creating a training set for use in training a statistical translation model, comprising:

constructing queries;

presenting the queries to an electrical data retrieval module, the retrieval module configured to perform a searching operation within an unstructured resource based on the queries;

receiving result sets from the retrieval module, the result sets providing result items identified by the retrieval module as a result of the searching operation; and

performing processing on the result sets to produce a structured training set, the training set identifying pairs of the result items within the result sets,

the training set providing a basis by which an electrical training system can learn the statistical translation model.

2. The method of claim 1, wherein the retrieval module is a search engine and wherein the unstructured resource is a collection resource items accessible via a network environment.

3. The method of claim 2, wherein the network environment is a wide area network.

4. The method of claim 1, wherein said performing processing includes constraining the result items in the result sets based on at least one consideration.

5. The method of claim 4, wherein said constraining includes identifying result items as candidates for pairwise matching based on ranking scores associated with the result items.

6. The method of claim 4, wherein said constraining includes identifying result items as candidates for pairwise matching based on agreement between the result items and respective lexical signatures associated with the result sets.

7. The method of claim 4, wherein said constraining includes identifying result items as candidates for pairwise matching based on similarity scores associated with respective pairs of result items.

8. The method of claim 4, wherein said constraining includes identifying candidates for pairwise matching based on associations between the result items and identified clusters of result items.

9. The method of claim 1, wherein said performing processing comprises, for each result set, identifying pairs of result items within the result set.

10. The method of claim 1, wherein the result items within the result sets correspond to monolingual text content.

11. The method of claim 1, wherein the result items within the result sets correspond to bilingual text content.

12. The method of claim 1, wherein the result items comprise text segments retrieved by the retrieval module from the unstructured resource, the text segments corresponding to excerpts of respective resource items within the unstructured resource.

13. The method of claim 1, further comprising generating the statistical translation model based on the training set and applying the statistical translation model, said applying comprising one of:

using the statistical translation model to expand a search query;

using the statistical translation model to facilitate a document indexing decision;

using the statistical translation model to revise text content;

or

using the statistical translation model to expand advertising information.

14. An electrical mining system for creating a training set for use in training a statistical translation model, comprising:

a query presentation module configured to construct queries;

an interface module configured to;

present the queries to a retrieval module, the retrieval module configured to perform a searching operation within an unstructured resource based on the queries; and

receive result sets from the retrieval module, the result sets providing result items identified by the retrieval module as a result of the searching operation; and

a training set preparation module configured to perform processing on the result sets to produce a structured training set, the training set identifying pairs of result items within the result sets,

the training set providing a basis by which an electrical training system can learn the statistical translation model,

the result items within the result sets comprising text segments retrieved by the retrieval module from the unstructured resource, the text segments corresponding to at least sentence fragments of respective resource items within the unstructured resource, the resource items having no pre-identified relation to each other.

15. The mining system of claim 14, wherein the result items within the result sets correspond to monolingual text content, the statistical translation model produced by the training system being used to map between semantically-related phrases within a single language.

16. The mining system claim 14, wherein the result items within the result sets correspond to bilingual text content, the statistical translation model produced by the training system being used to map between phrases within two respective languages.

17. A computer readable medium for storing computer readable instructions, the computer readable instructions providing a mining system when executed by one or more processing devices, the computer readable instructions comprising:

interface logic configured to retrieve result items from an unstructured resource on the basis of queries submitted to the unstructured resource, the unstructured resource corresponding to network-accessible resource items; and

training set preparation logic configured to establish a structured training set from the result items retrieved from the unstructured resource, the training set being constructed in a manner which is agnostic with respect to any similarity among the resource items as respective wholes and any parallelism within sentences contained within the resource items,

the training set providing a basis by which an electrical training system can learn a statistical translation model.

18. The computer readable medium of claim 17, wherein the result items within the result sets comprise text segments retrieved from the unstructured resource, the text segments corresponding to excerpts of respective resource items within the unstructured resource.

19. The computer readable of claim 17, wherein the result items within the result sets correspond to monolingual text content, the statistical translation model produced by the training system being used to map between semantically-related phrases within a single language.

20. The computer readable medium of claim 17, wherein the result items within the result sets correspond to bilingual text content, the statistical translation model produced by the training system being used to map between phrases within two respective languages.