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Statistical models for the induction and use of selectional preferences
Author(s) -
Light Marc,
Greiff Warren
Publication year - 2002
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1207/s15516709cog2603_4
Subject(s) - computer science , hierarchy , inference , artificial intelligence , computational linguistics , natural language processing , generative grammar , class (philosophy) , simple (philosophy) , statistical inference , statistical model , bayesian probability , generative model , machine learning , mathematics , statistics , epistemology , philosophy , economics , market economy
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 show how they together form a cohesive line of research.