Annotating entities using cross-document signals

• US 9,275,135 B2
• Filed: 05/29/2012
• Issued: 03/01/2016
• Est. Priority Date: 05/29/2012
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: De, Sushovan, Singh, Amit K., Visweswariah, Karthik
• Primary Examiner(s): Baderman, Scott
• Assistant Examiner(s): EDWARDS, JASON T
• Application Number:
  US13/482,177
• Publication Number:
  US 20130326325A1
• Time in Patent Office: 1,372 Days
• Field of Search: 704/4, 704/8, 704/9, 704/10, 706/46, 706/47, 706/56, 707/1, 707/2, 707/3, 707/4, 707/5, 707/7, 707/23, 707/737, 707/740, 715/200, 715/230
• US Class Current: 1/1
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification

Abstract

An apparatus and an article of manufacture for annotating an entity in a document corpus using cross-document signals include determining which documents in a document corpus mention an entity of interest, clustering the documents that mention an entity of interest according to a temporal signal, a structural signal and/or a content signal, thereby forming at least one cluster of documents, and annotating at least one document in the at least one cluster of documents by marking each occurrence of the entity in the at least one document.

Claims

1. An article of manufacture comprising a non-transitory computer readable storage medium having computer readable instructions tangibly embodied thereon which, when implemented, cause a computer to carry out a plurality of method steps comprising:

determining which documents in a document corpus of multiple documents mention an entity of interest;

clustering the documents that mention an entity of interest according to similarities across a temporal signal, a structural signal and a content signal, thereby forming multiple clusters of documents;

annotating each document in the multiple clusters of documents with an annotation by marking each occurrence of the entity in each document;

calculating a confidence measure for each occurrence of the entity in each document in each of the multiple clusters, wherein said confidence measure comprises the sum of (i) a measure of similarity between the given occurrence of the entity and the entity of interest, and (ii) a measure of similarity between the documents within the cluster of the given document via

2. The article of manufacture of claim 1, wherein said determining comprises using a dictionary of entities.

3. The article of manufacture of claim 1, wherein the method steps comprise marking an occurrence of the entity in at least one document as not applicable, in order to opt out of at least one of an incorrect and an uncertain annotation.

4. The article of manufacture of claim 1, wherein the method steps comprise training a set of documents with labeled entities to determine a threshold for clustering documents and/or a document similarity weight.

5. The article of manufacture of claim 1, wherein the method steps comprise annotating a single entity in a single document.

6. The article of manufacture of claim 5, wherein the method steps comprise determining an entity-to-context match for a mention of the entity from the document in an entity database without considering a signal from other documents or other entities in the document.

7. The article of manufacture of claim 1, wherein the method steps comprise annotating multiple entities in a single document.

8. The article of manufacture of claim 7, wherein the method steps comprise determining an entity-to-context match for each mention of the multiple entities from the document in an entity database without considering a signal from other documents.

9. The article of manufacture of claim 1, wherein the method steps comprise annotating a single entity in multiple documents.

10. The article of manufacture of claim 9, wherein the method steps comprise determining at least one of an entity-to-context match and a document-to-document match for a mention of the entity from the document in an entity database, taking into account similarity and temporal proximity to other documents without considering a signal from other entities in the document.

11. The article of manufacture of claim 1, wherein the method steps comprise annotating multiple entities in multiple documents.

12. The article of manufacture of claim 11, wherein said determining includes considering signals from multiple entities in each document as well as temporal and textual similarity to other documents in the corpus.

13. A system for annotating an entity in a document corpus using cross-document signals, comprising:

a memory; and

at least one processor coupled to the memory and operative for;

determining which documents in a document corpus of multiple documents mention an entity of interest;

clustering the documents that mention an entity of interest according to similarities across a temporal signal, a structural signal and a content signal, thereby forming multiple clusters of documents;

annotating each document in the multiple clusters of documents with an annotation by marking each occurrence of the entity in each document;

calculating a confidence measure for each occurrence of the entity in each document in each of the multiple clusters, wherein said confidence measure comprises the sum of (i) a measure of similarity between the given occurrence of the entity and the entity of interest, and (ii) a measure of similarity between the documents within the cluster of the given document via

14. An article of manufacture comprising a non-transitory computer readable storage medium having computer readable instructions tangibly embodied thereon which, when implemented, cause a computer to carry out a plurality of method steps comprising:

processing each document in a corpus of multiple documents obtained from at least one online source to identify which documents mention an entity of interest;

using a description of the entity derived from a database to generate at least one context feature for the entity;

processing each of the documents that mention the entity of interest by comparing text from each document with the at least one context feature and grouping the documents with a comparison similarity above a pre-determined threshold into a cluster of documents;

annotating each document in the cluster of documents with an annotation by marking each occurrence of the entity in each document;

calculating a confidence measure for each occurrence of the entity in each document in the cluster of documents, wherein said confidence measure comprises the sum of (i) the comparison similarity between the given occurrence of the entity and the entity of interest, (ii) a measure of similarity between the documents within the cluster of documents via