Automatic extraction of synonyms and/or semantically related words has various applications in Natural Language Processing (NLP). There are currently two mainstream extraction paradigms, namely, lexicon-based and distributional approaches. The former usually suffers from low coverage, while the latter is only able to capture general relatedness rather than strict synonymy.
In this paper, two rule-based extraction methods are applied to denitions from a machine-readable dictionary. Extracted synonyms are evaluated in two experiments by solving TOEFL synonym questions and being compared against existing thesauri. The proposed approaches have achieved satisfactory results in both evaluations, comparable to published studies or even the state of the art.
@MastersThesis{ wang-msc,
author = {Tong Wang},
title = {Extracting Synonyms from Dictionary Definitions},
school = {Department of Computer Science, University of Toronto},
year = {2009},
abstract = {<p>Automatic extraction of synonyms and/or semantically
related words has various applications in Natural Language
Processing (NLP). There are currently two mainstream
extraction paradigms, namely, lexicon-based and
distributional approaches. The former usually suffers from
low coverage, while the latter is only able to capture
general relatedness rather than strict synonymy.</p> <p> In
this paper, two rule-based extraction methods are applied
to denitions from a machine-readable dictionary. Extracted
synonyms are evaluated in two experiments by solving TOEFL
synonym questions and being compared against existing
thesauri. The proposed approaches have achieved
satisfactory results in both evaluations, comparable to
published studies or even the state of the art. </p>},
download = {http://ftp.cs.toronto.edu/pub/gh/Wang-MSc-paper.pdf}
}