The quantification of lexical semantic relatedness has many applications in NLP, and many different measures have been proposed. We evaluate five of these measures, all of which use WordNet as their central resource, by comparing their performance in detecting and correcting real-word spelling errors. An information-content-based measure proposed by Jiang and Conrath is found superior to those proposed by Hirst and St-Onge, Leacock and Chodorow, Lin, and Resnik. In addition, we explain why distributional similarity is not an adequate proxy for lexical semantic relatedness.
Description
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
%0 Journal Article
%1 Budanitsky06semanticsWordNet
%A Budanitsky, Alexander
%A Hirst, Graeme
%C Cambridge, MA, USA
%D 2006
%I MIT Press
%J Comput. Linguist.
%K 06 Budanitsky WordNet lexical relatedness semantics
%N 1
%P 13--47
%R http://dx.doi.org/10.1162/coli.2006.32.1.13
%T Evaluating WordNet-based Measures of Lexical Semantic Relatedness
%U http://portal.acm.org/citation.cfm?id=1168108
%V 32
%X The quantification of lexical semantic relatedness has many applications in NLP, and many different measures have been proposed. We evaluate five of these measures, all of which use WordNet as their central resource, by comparing their performance in detecting and correcting real-word spelling errors. An information-content-based measure proposed by Jiang and Conrath is found superior to those proposed by Hirst and St-Onge, Leacock and Chodorow, Lin, and Resnik. In addition, we explain why distributional similarity is not an adequate proxy for lexical semantic relatedness.
@article{Budanitsky06semanticsWordNet,
abstract = {The quantification of lexical semantic relatedness has many applications in NLP, and many different measures have been proposed. We evaluate five of these measures, all of which use WordNet as their central resource, by comparing their performance in detecting and correcting real-word spelling errors. An information-content-based measure proposed by Jiang and Conrath is found superior to those proposed by Hirst and St-Onge, Leacock and Chodorow, Lin, and Resnik. In addition, we explain why distributional similarity is not an adequate proxy for lexical semantic relatedness.},
added-at = {2010-03-05T02:35:30.000+0100},
address = {Cambridge, MA, USA},
author = {Budanitsky, Alexander and Hirst, Graeme},
biburl = {https://www.bibsonomy.org/bibtex/2234d80378f6e6f1bc2dc77efa96a3545/lee_peck},
description = {Evaluating WordNet-based Measures of Lexical Semantic Relatedness},
doi = {http://dx.doi.org/10.1162/coli.2006.32.1.13},
interhash = {a259f21d89bdc61a64ce11a3aea0af06},
intrahash = {234d80378f6e6f1bc2dc77efa96a3545},
issn = {0891-2017},
journal = {Comput. Linguist.},
keywords = {06 Budanitsky WordNet lexical relatedness semantics},
number = 1,
pages = {13--47},
publisher = {MIT Press},
timestamp = {2010-03-05T02:35:30.000+0100},
title = {Evaluating WordNet-based Measures of Lexical Semantic Relatedness},
url = {http://portal.acm.org/citation.cfm?id=1168108},
volume = 32,
year = 2006
}