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A Supervised Learning Approach to
Search of Definitions
April 1, 2006
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Download Document
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BibTex
Authors

Jun Xu

Yunbo Cao

Hang Li

Min Zhao

Yalou Huang
Publication Type
Inproceedings
Pages
19
Number
MSR-TR-2006-18

Abstract

Related Info
Abstract
This paper addresses the issue of search of definitions. Specifically, given a term, we are to find
definition candidates of the term and rank the candidates according to their likelihood of being
good definitions. This is in contrast to the traditional approaches of either generating a single
combined definition or outputting all retrieved definitions. Necessity of conducting the task in
practice is pointed out. Definition ranking is essential for the task. A specification for judging the
goodness of a definition is given. In the specification, a definition is categorized into one of the
three levels: ‘good definition’, ‘indifferent defi-nition’, or ‘bad definition’. Methods for
performing definition ranking are also proposed in this paper, which formalize the problem as
either classification or ordinal regression. We employ SVM (Support Vector Machines) as the
classification model and Ranking SVM as the ordinal regression model respec-tively, such that
they rank definition candidates according to their likelihood of being good definitions. Features
for constructing the SVM and Ranking SVM models are defined, which represent the characteristics of term, definition candidate, and their relationship. Experimental results indicate that the
use of SVM and Ranking SVM can significantly outperform the baseline methods of using
heuristic rules, em-ploying the conventional information retrieval method of Okapi, or using
SVM regression. This is true both when the answers are paragraphs and when they are sentences.
Experimental results also show that SVM or Ranking SVM models trained in one domain can be
adapted to another domain, indicating that generic models for definition ranking can be
constructed.
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