Methods and systems for automatically summarizing semantic properties from documents with freeform textual annotations

• US 20100153318A1
• Filed: 11/19/2009
• Published: 06/17/2010
• Est. Priority Date: 11/19/2008
• Status: Abandoned Application

• Current Assignee: Massachusetts Institute of Technology
• Original Assignee: Massachusetts Institute of Technology
• Inventors: Eisenstein, Jacob Richard, Branavan, Satchuthananthavale Rasiah Kuhan, Chen, Harr, Barzilay, Regina
• Application Number:
  US12/592,128
• Publication Number:
  US 20100153318A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 40/169 Annotation, e.g. comment data or footnotes

Abstract

Some embodiments are directed to identifying semantic properties of documents using free-text annotations associated with the documents. Semantic properties of documents may be identified by using a model that is trained on a corpus of training documents where one or more of the training documents may include free-text annotations. In some embodiments, the model may identify semantic topics expressed only in free-text annotations or only in the body of a document. The model may applied to identify semantic topics associated with a work document or to summarize the semantic topics present in a plurality of work documents.

Claims

1. A method comprising acts of:

(A) using free-text annotations in a set of training documents to create a model to identify semantic topics associated with the training documents; and

(B) applying the model to at least one work document to identify at least one semantic topic associated with the at least one work document.

2. The method of claim 1, wherein the act (A) comprises an act of using the set of training documents to create a model that is able to identify two or more documents from the set of training documents as being associated with a same semantic topic even when the free-text annotations in the two or more documents use different words.

3. The method of claim 1, wherein the act (A) comprises an act of using the set of training documents to create a model that is able to learn a relationship among different free-text annotations in the training documents.

4. The method of claim 1, wherein the act (A) comprises an act of using the set of training documents to create a model that is able to identify a work document as being associated with a semantic topic even when the work document does not include the same free-text annotations as the training documents that are associated with the semantic topic.

5. The method of claim 1, wherein the act (A) comprises an act of using the set of training documents to create a model that is able to identify a free-text annotation of a work document as being associated with a semantic topic, even when the free-text annotation does not appear in the set of training documents.

6. The method of claim 1, wherein the act (A) comprises an act of using the set of training documents to create a model that is able to identify free-text annotations as being associated with a same semantic topic even when the free-text annotations use different words.

7. The method of claim 1, further comprising acts of:

(C) assigning similarity scores to some of the free-text annotations, wherein a similarity score for a particular free-text annotation provides an indication of how similar the particular free-text annotation is to other free-text annotations;

(D) providing the similarity scores to the model.

8. The method of claim 7, wherein the act (C) comprises evaluating at least one piece of information in addition to word distributions in the free-text annotations in assigning similarity scores.

9. The method of claim 1, wherein the act (A) comprises using the free-text annotations in the set of training documents to create a model comprising a first sub-model and a second sub-model, wherein the first sub-model examines free-text annotations in the at least one work document for one or more semantic topics, wherein the second sub-model examines a body of the at least one work document for one or more semantic topics, and wherein the first sub-model and the second sub-model are linked.

10. The method of claim 1, further comprising acts of:

(C) applying the model to at least one other work document to identify at least one other semantic topic associated with the at least one other work document;

(D) creating a summary of the at least one work document and the at least one other work document.

11. The method of claim 1, wherein the act (A) comprises an act of using a set of training documents that does not include professional annotations.

12. The method of claim 1, wherein the act (A) comprises an act of using a set of training documents comprising at least some training documents that do not include professional annotations.

13. A system comprising at least one processor programmed to:

(A) use free-text annotations in a set of training documents to create a model to identify semantic topics associated with the training documents; and

(B) apply the model to at least one work document to identify at least one semantic topic associated with the at least one work document.

14. The system of claim 13, wherein the model is able to identify two or more documents from the set of training documents as being associated with a same semantic topic even when the free-text annotations in the two or more documents use different words.

15. The system of claim 13, wherein the model is able to identify a work document as being associated with a semantic topic even when the work document does not include the same free-text annotations as the training documents that are associated with the semantic topic.

16. The system of claim 13, wherein the model is able to identify a free-text annotation of a work document as being associated with a semantic topic, even when the free-text annotation does not appear in the set of training documents.

17. The system of claim 13, wherein the model is able to identify free-text annotations as being associated with a same semantic topic even when the free-text annotations use different words.

18. The system of claim 13, wherein the at least one processor is further programmed to:

(C) assign similarity scores to some of the free-text annotations, wherein a similarity score for a particular free-text annotation provides an indication of how similar the particular free-text annotation is to other free-text annotations;

(D) provide the similarity scores to the model.

19. The system of claim 18, wherein the similarity scores are based on evaluating at least one piece of information in addition to word distributions in the free-text annotations.

20. The system of claim 13, wherein the model comprises a first sub-model and a second sub-model, wherein the first sub-model examines free-text annotations in the at least one work document for one or more semantic topics, wherein the second sub-model examines a body of the at least one work document for one or more semantic topics, and wherein the first sub-model and the second sub-model are linked.

21. The system of claim 13, wherein the at least one processor is further programmed to:

(C) apply the model to at least one other work document to identify at least one other semantic topic associated with the at least one other work document;

(D) create a summary of the at least one work document and the at least one other work document.

22. The system of claim 13, wherein the training documents do not include professional annotations.

23. At least one computer readable storage medium encoded with instructions that, when executed, perform a method comprising acts of:

(A) using free-text annotations in a set of training documents to create a model to identify semantic topics associated with the training documents; and

(B) applying the model to at least one work document to identify at least one semantic topic associated with the at least one work document.

24. The at least one computer readable storage medium of claim 23, wherein the act (A) comprises an act of using the set of training documents to create a model that is able to identify two or more documents from the set of training documents as being associated with a same semantic topic even when the free-text annotations in the two or more documents use different words.

25. The at least one computer readable storage medium of claim 23, wherein the act (A) comprises an act of using the set of training documents to create a model that is able to identify a work document as being associated with a semantic topic even when the work document does not include the same free-text annotations as the training documents that are associated with the semantic topic.

26. The at least one computer readable storage medium of claim 23, wherein the act (A) comprises an act of using the set of training documents to create a model that is able to identify a free-text annotation of a work document as being associated with a semantic topic, even when the free-text annotation does not appear in the set of training documents.

27. The at least one computer readable storage medium of claim 23, wherein the method further comprises acts of:

(C) assigning similarity scores to some of the free-text annotations, wherein a similarity score for a particular free-text annotation provides an indication of how similar the particular free-text annotation is to other free-text annotations;

(D) providing the similarity scores to the model.

28. The at least one computer readable storage medium of claim 27, wherein the act (C) comprises evaluating at least one piece of information in addition to word distributions in the free-text annotations in assigning similarity scores.

29. The at least one computer readable storage medium of claim 23, wherein the act (A) comprises using the free-text annotations in the set of training documents to create a model comprising a first sub-model and a second sub-model, wherein the first sub-model examines free-text annotations in the at least one work document for one or more semantic topics, wherein the second sub-model examines a body of the at least one work document for one or more semantic topics, and wherein the first sub-model and the second sub-model are linked.

30. The at least one computer readable storage medium of claim 23, wherein the method further comprises acts of:

(C) applying the model to at least one other work document to identify at least one other semantic topic associated with the at least one other work document;

(D) creating a summary of the at least one work document and the at least one other work document.

31. The at least one computer readable storage medium of claim 23, wherein the act (A) comprises an act of using a set of training documents that does not include professional annotations.

32. A method for creating a model to associate one or more work documents with one or more semantic topics, the method comprising acts of:

(A) using a set of training documents that include annotations;

(B) assigning similarity scores to some of the annotations, wherein a similarity score for a particular annotation provides an indication of how similar the particular annotation is to other annotations;

(C) providing the similarity scores to the model.

33. The method of claim 32, wherein the act (B) comprises evaluating at least one piece of information in addition to word distributions in the annotations in assigning similarity scores.

34. The method of claim 32, wherein the annotations are free-text annotations.

35. A system comprising:

at least one processor programmed to create a model to associate one or more work documents with one or more semantic topics by;

using a set of training documents that include annotations;

assigning similarity scores to some of the annotations, wherein a similarity score for a particular annotation provides an indication of how similar the particular annotation is to other annotations;

providing the similarity scores to the model.

36. The system of claim 35, wherein the at least one processor is programmed to assign the similarity scores by evaluating at least one piece of information in addition to word distributions in the annotations in assigning the similarity scores.

37. The system of claim 35, wherein the annotations are free-text annotations.

38. At least one computer readable storage medium encoded with instructions that, when executed, perform a method for creating a model to associate one or more work documents with one or more semantic topics, the method comprising acts of:

(A) using a set of training documents that include annotations;

(B) assigning similarity scores to some of the annotations, wherein a similarity score for a particular annotation provides an indication of how similar the particular annotation is to other annotations;

(C) providing the similarity scores to the model.

39. The at least one computer readable storage medium of claim 38, wherein the act (B) comprises evaluating at least one piece of information in addition to word distributions in the annotations in assigning similarity scores.

40. The at least one computer readable storage medium of claim 38, wherein the annotations are free-text annotations.