IP Library Patent Application 12341926
Patent Application
App. No. 12/341,926

SYSTEMS, METHODS, AND SOFTWARE FOR ENTITY EXTRACTION AND RESOLUTION COUPLED WITH EVENT AND RELATIONSHIP EXTRACTION

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Quick Facts
Patent No.
US None
App. No.
12/341,926
Abstract

For automated text processing, the inventors devised, among other things, an exemplary system that includes an entity tagger, an entity resolver, a text segment classifier, and a relationship extractor. The entity tagger receives an input text segment, and tags named entities with the segment as being a person, company, or place. The entity resolver accesses authority files, and associates the persons and companies named in the text segment with specific entries in the files. The text segment classifier determines whether the text segment includes a relationship event, such as job-change event or merger and acquisition event, and if an event is detected, the relationship extractor determines the event role of entities named in the segment. For example, the extractor determines for a merger and acquisition event, which named company was the acquirer and which was acquired.

Claims (27)

1 . A computer system having at least one processor and at least one memory, the system comprising:

means for automatically tagging entity names within a text segment as being one of a person, company, and location; and

means for logically associating one or more of the tagged entity names with an entry in a data set of named entities.

2 . The system of claim 1 , wherein the means for tagging entity names within a text segment, includes:

means for automatically pretagging one or more portions of the text segment as being one of a person, company, and location based on a list or rule; and

a statistical sequence decoder, responsive to the means for pretagging, for tagging other portions of the text segment as being one of a person, company, or location.

3 . The system of claim 2 , wherein the means for pretagging includes a list of company names.

4 . The system of claim 2 , wherein the means for pretagging includes a set of one or more text pattern rules.

5 . The system of claim 2 , wherein the statistical sequence decoder includes a Viterbi decoder.

6 . The system of claim 1 , wherein the means for tagging entity names outputs a character positions for each tagged named entity.

7 . The system of claim 1 , further comprising:

means for automatically classifying a tagged text segment as having a minimal number of tagged entities to form a relationship of interest having at least first and second roles; and

means, responsive to the classifying means, for automatically determining which of the tagged entities in the tagged text segment that is classified as having a minimal number of tagged entities has the first role and which has the second role.

8 . A computer implemented method comprising:

automatically tagging entity names within a text segment as being one of a person, company, and location; and

automatically associating one or more of the tagged entity names with an entry in a data set of named entities.

9 . The method of claim 8 , wherein automatically tagging entity named within the text segment, includes:

pretagging one or more portions of the text segment as being one of a person, company, and location based on a list or rule; and

using a statistical sequence decoder to tag other portions of the text segment as being one of a person, company, or location.

10 . The method of claim 9 , wherein the statistical sequence decoder includes a Viterbi decoder.

11 . The method of claim 8 , further comprising:

automatically classifying a tagged text segment as having a minimal number of tagged entities to form a relationship of interest having at least first and second roles; and

automatically determining which of the tagged entities in the tagged text segment that is classified as having a minimal number of tagged entities has the first role and which has the second role.

12 . A computer-implemented method comprising:

automatically tagging one or more portions of a text segment as being one of a person, company, and location based on a list or rule; and

using a statistical sequence decoder to tag other portions of the text segment as being one of a person, company, or location.

13 . The method of claim 12 , wherein the statistical sequence decoder includes a Viterbi decoder.

Assignments (4)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 5, 2020
From: THOMSON REUTERS GLOBAL RESOURCES UNLIMITED COMPANY
To: THOMSON REUTERS ENTERPRISE CENTRE GMBH
Reel/Frame 052028/0531 →
CHANGE OF NAME Recorded Nov 30, 2017
From: THOMSON REUTERS GLOBAL RESOURCES
To: THOMSON REUTERS GLOBAL RESOURCES UNLIMITED COMPANY
Reel/Frame 044263/0539 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 23, 2009
From: LIGHT, MARC; SCHILDER, FRANK; KONDADADI, RAVI KUMAR; DOZIER, CHRISTOPHER C.; LIAO, WENHUI; VEERAMACHANENI, SRIHARSHA
To: WEST SERVICES, INC.
Reel/Frame 023272/0684 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 23, 2009
From: WEST SERVICES, INC.
To: THOMSON REUTERS GLOBAL RESOURCES
Reel/Frame 023277/0015 →