Statistical Models for the Induction and Use of Selectional Preferences
March 2002
Marc Light, The MITRE Corporation
Warren Greiff, The MITRE Corporation
ABSTRACT
Selectional preferences have a long history in both generative and computational linguistics. However, since the publication of Resnik’s dissertation in 1993, a new approach has surfaced in the computational linguistics community. This new line of research combines knowledge represented in a pre-defined semantic class hierarchy with statistical tools including information theory, statistical modeling, and Bayesian inference. These tools are used to learn selectional preferences from examples in a corpus. Instead of simple sets of semantic classes, selectional preferences are viewed as probability distributions over various entities. We survey research that extends Resnik’s initial work, discuss the strengths and weaknesses of each approach, and showhowthey together form a cohesive line of research. ©2002 Published by Cognitive Science Society, Inc.
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Additional Search Keywords
Computational linguistics, Selectional preferences, Statistical modeling, Learning
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