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A general feature space for automatic verb classification

Published online by Cambridge University Press:  01 July 2008

ERIC JOANIS ,
SUZANNE STEVENSON  and
DAVID JAMES
ERIC JOANIS*
Affiliation:
Department of Computer Science, University of Toronto, 6 King's College Road, Toronto, Ontario, Canada, M5S 3H5 e-mail: joanis@cs.toronto.edu, suzanne@cs.toronto.edu, james@cs.toronto.edu
SUZANNE STEVENSON
Affiliation:
Department of Computer Science, University of Toronto, 6 King's College Road, Toronto, Ontario, Canada, M5S 3H5 e-mail: joanis@cs.toronto.edu, suzanne@cs.toronto.edu, james@cs.toronto.edu
DAVID JAMES
Affiliation:
Department of Computer Science, University of Toronto, 6 King's College Road, Toronto, Ontario, Canada, M5S 3H5 e-mail: joanis@cs.toronto.edu, suzanne@cs.toronto.edu, james@cs.toronto.edu
*
†Current affiliation: Interactive Language Technologies Group, Institute for Information Technology, National Research Council Canada, A1330-101 St-Jean-Bosco Street, Gatineau, Quebec, CanadaJ8Y 3G5.
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Abstract

Lexical semantic classes of verbs play an important role in structuring complex predicate information in a lexicon, thereby avoiding redundancy and enabling generalizations across semantically similar verbs with respect to their usage. Such classes, however, require many person-years of expert effort to create manually, and methods are needed for automatically assigning verbs to appropriate classes. In this work, we develop and evaluate a feature space to support the automatic assignment of verbs into a well-known lexical semantic classification that is frequently used in natural language processing. The feature space is general – applicable to any class distinctions within the target classification; broad – tapping into a variety of semantic features of the classes; and inexpensive – requiring no more than a POS tagger and chunker. We perform experiments using support vector machines (SVMs) with the proposed feature space, demonstrating a reduction in error rate ranging from 48% to 88% over a chance baseline accuracy, across classification tasks of varying difficulty. In particular, we attain performance comparable to or better than that of feature sets manually selected for the particular tasks. Our results show that the approach is generally applicable, and reduces the need for resource-intensive linguistic analysis for each new classification task. We also perform a wide range of experiments to determine the most informative features in the feature space, finding that simple, easily extractable features suffice for good verb classification performance.

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Type
Papers
Information
Natural Language Engineering , Volume 14 , Issue 3 , July 2008 , pp. 337 - 367
Copyright
Copyright © Cambridge University Press 2006

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