Diverse Topic Phrase Extraction

• US 20080208840A1
• Filed: 09/21/2007
• Published: 08/28/2008
• Est. Priority Date: 02/22/2007
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Chen, Zheng, Zhang, Benyu, Wang, Jian, Zeng, HuaJun, Chen, Jilin
• Granted Patent: US 8,280,877 B2
• Time in Patent Office: Days
• US Class Current: 707/5
• CPC Class Codes: G06F 40/258 Heading extraction; Automatic titling; Numbering
• CPC Class Codes: G06F 40/295 Named entity recognition

Abstract

Systems and methods for implementing diverse topic phrase extraction are disclosed. According to one implementation, multiple word candidate phrases are extracted from a corpus and weighed. One or more documents are re-weighed to identify less obvious candidate topics using latent semantic analysis (LSA). Phrase diversification is then used to remove redundancy and select informative and distinct topic phrases.

Claims

1. A method of summarizing search results from a corpus comprising:

re-weighting a plurality of weighted documents based at least in part on a latent topic to identify one or more topic phrases associated with candidate phrases occurring within one or more documents;

filtering the documents having similar topic phrases to remove redundancy of the topic phrases.

2. The method of claim 1, wherein re-weighting comprises:

selecting the one or more latent topics from the corpus, each of the latent topic being characterized by a singular value, wherein the singular value indicates the relevance of the topic phrases associated with the documents;

arranging the latent topics based on the singular values;

extracting at least one phrase from the latent topics.

3. The method of claim 1, wherein the contents associated with the documents are based at least on an association between a document and its constituent phrases.

4. The method of claim 3, wherein the association between the documents and the constituent phrases is implemented using latent semantic analysis.

5. The method of claim 2, further comprising associating a weighted frequency value based on the singular value and the associated latent topic.

6. The method of claim 1, wherein the filtering comprises:

evaluating a dissimilarity score between one or more phrases in the documents;

filtering phrases based at least on the basis of the dissimilarity score.

7. The method of claim 6, wherein the filtering comprises removing phrases with a high value of the dissimilarity score, the dissimilarity score indicating the extent of dissimilarity between the phrases.

8. A system comprising:

one or more processors;

a memory;

a re-weighting module for identifying one or more topic phrases associated with at least one document;

a diversification module for removing documents associated with one or more similar concepts to result in a list of documents with unlike concepts.

9. The system of claim 8, wherein the re-weighting module:

associates the documents with a first weight factor on the basis of the concepts associated with the documents, the concepts being characterized by a latent topic;

associates the documents with a second weight factor based at least on the latent topic.

10. The system of claim 9, wherein the re-weighting module further:

arranges the latent topics based on the first weight factor;

extracts at least one phrase from the latent topics.

11. The system of claim 8, wherein the concepts associated with the documents are based at least on an association between the document and its constituent phrases.

12. The system of claim 11, wherein the association is implemented using latent semantic analysis.

13. The system of claim 8, wherein the diversification module:

calculates a similarity score between one or more phrases in the documents;

and retains one or more phrases based on the similarity score.

14. The system of claim 13, wherein the diversification module removes phrases with similar values of a similarity score.

15. One or more computer-readable media comprising computer executable instructions that, when executed, direct a computing system to:

re-weighting a plurality of documents in a corpus based at least in part on a latent topic to identify one or more concepts associated with the documents;

filtering the documents associated with similar concepts to result in a document collection with unlike concepts.

16. The computer readable media of claim 15, wherein the re-weighting comprises:

selecting the one or more latent topics from the corpus, each of the latent topic being characterized by a singular value, wherein the singular value indicates the relevance of the concepts associated with the documents;

arranging the latent topics based on the singular values; and

extracting at least one phrase from the latent topics.

17. The computer readable media of claim 15, wherein the concepts associated with the documents are based at least on an association implemented using latent semantic analysis.

18. The computer readable media of claim 15, wherein the filtering comprises:

evaluating a dissimilarity score between one or more phrases in the documents;

sifting phrases based at least on the basis of the dissimilarity score.

19. The computer readable medium of claim 18, wherein the sifting comprises removing phrases with a high value of the dissimilarity score, the dissimilarity score indicating the extent of dissimilarity between the phrases.

20. The computer readable media of claim 18, wherein the dissimilarity score is dependent on order of appearance of words in the phrases.