Method and System for Word Sequence Processing

• US 20110246076A1
• Filed: 05/28/2005
• Published: 10/06/2011
• Est. Priority Date: 05/28/2004
• Status: Abandoned Application

• Current Assignee: Agency For Science Technology and Research (Government of Singapore)
• Original Assignee: Agency For Science Technology and Research (Government of Singapore)
• Inventors: Shen, Dan, Su, Jian, Zhou, Guo Dong, Zhang, Jie
• Application Number:
  US11/597,801
• Publication Number:
  US 20110246076A1
• Time in Patent Office: Days
• US Class Current: 702/19
• CPC Class Codes: G06F 18/00 Pattern recognition
• CPC Class Codes: G06F 40/295 Named entity recognition
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]

Abstract

A method and system of conducting named entity recognition. One method comprises selecting one or more examples for human labelling, each example comprising a word sequence containing a named entity and its context; and retraining a model for the named entity recognition based on the labelled examples as training data.

Claims

1. A method of conducting named entity recognition, the method comprising

selecting one or more examples for human labelling, each example comprising a word sequence containing a named entity and its context;

and

retraining a model for the named entity recognition based on the labelled examples as training data.

2. The method as claimed in claim 1, wherein the selecting is based on one or more criteria of a group consisting of an informativeness criterion, a representativeness criterion, and a diversity criterion.

3. The method as claimed in claim 2, wherein the selecting further comprises applying a strategy comprising two or more of the criteria in a selected sequence.

4. The method as claimed in claim 3, wherein the strategy comprises combining two or more of the criteria into a single criteria.

5. A method of conducting a word sequence processing task, the method comprising

selecting one or more examples for human labelling based on an informativeness criterion, a representativeness criterion, and a diversity criterion, and

retraining a model for the named entity recognition based on the labelled examples as training data.

6. The method as claimed in claim 5, wherein the word sequence processing task comprises one or more of a group consisting of POS tagging, text chunking and parsing.

7. A system for conducting named entity recognition, the system comprising

a selector for selecting one or more examples for human labelling, each example comprising a word sequence containing a named entity and its context;

and

a processor for retraining a model for the named entity recognition based on the labelled examples as training data.

8. A system for conducting a word sequence processing task, the system comprising

a selector for selecting one or more examples for human labelling based on an informativeness criterion, a representativeness criterion, and a diversity criterion, and

a processor for retraining a model for the named entity recognition based on the labelled examples as training data.

9. A data storage medium having stored thereon computer code means for instructing a computer to execute a method of conducting named entity recognition, the method comprising

selecting one or more examples for human labelling, each example comprising a word sequence containing a named entity and its context;

and

retraining a model for the named entity recognition based on the labelled examples as training data.

10. A data storage medium having stored thereon computer code means for instructing a computer to execute a method of conducting a word sequence processing task, the method comprising

selecting one or more examples for human labelling based on an informativeness criterion, a representativeness criterion, and a diversity criterion, and

retraining a model for the named entity recognition based on the labelled examples as training data.