Abstract

There is an ever-growing need to add structure in the form of semantic markup to the huge amounts of unstructured text data now available. We present the technique of shallow semantic parsing, the process of assigning a simple WHO did WHAT to WHOM, etc., structure to sentences in text, as a useful tool in achieving this goal. We formulate the semantic parsing problem as a classification problem using support vector machines. Using a hand-labeled training set and a set of features drawn from earlier work together with some feature enhancements, we demonstrate a system that performs better than all other published results on shallow semantic parsing.

Meeting Name

3rd IEEE International Conference on Data Mining, ICDM 2003 (2003: Nov. 19-22, Melbourne, FL)

Department(s)

Psychological Science

International Standard Book Number (ISBN)

0-7695-1978-4

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2003 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Full Text Link

Included in

Psychology Commons

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