Paper Info
Title
Class-based probability estimation using a semantic hierarchy
Abstract
This article concerns the estimation of a particular kind of probability, namely, the probability of a noun sense appearing as a particular argument of a predicate. In order to overcome the accompanying sparse-data problem, the proposal here is to define the probabilities in terms of senses from a semantic hierarchy and exploit the fact that the senses can be grouped into classes consisting of semantically similar senses. There is a particular focus on the problem of how to determine a suitable class for a given sense, or, alternatively, how to determine a suitable level of generalization in the hierarchy. A procedure is developed that uses a chi-square test to determine a suitable level of generalization. In order to test the performance of the estimation method, a pseudo-disambiguation task is used, together with two alternative estimation methods. Each method uses a different generalization procedure; the first alternative uses the minimum description length principle, and the second uses Resnik's measure of selectional preference. In addition, the performance of our method is investigated using both the standard Pearson chi-square statistic and the log-likelihood chi-square statistic.
Year
DOI
Venue
2001
10.1162/089120102760173643
Computational Linguistics
Keywords
DocType
Volume
suitable level,argument slot,paper concern,particular argument,lexical knowledge,particular focus,log-likelihood chi-square statistic,different generalization procedure,suitable class,estimation method,alternative estimation method,semantic hierarchy,pseudo disambiguation task,different class-based estimation method,standard pearson chi-square statistic,class-based probability estimation,chi-square test,particular kind,noun sense,noun
Conference
28
Issue
ISSN
Citations 
2
0891-2017
80
PageRank 
References 
Authors
6.41
18
2
Name
Order
Citations
PageRank
Stephen Clark1806.41
David J. Weir284083.84