Using measures of semantic relatedness for word sense disambiguation

Siddharth Patwardhan, Satanjeev Banerjee, Ted Pedersen

Research output: Contribution to journalArticlepeer-review

241 Scopus citations

Abstract

This paper generalizes the Adapted Lesk Algorithm of Banerjee and Pedersen (2002) to a method of word sense disambiguation based on semantic relatedness. This is possible since Lesk's original algorithm (1986) is based on gloss overlaps which can be viewed as a measure of semantic relatedness. We evaluate a variety of measures of semantic relatedness when applied to word sense disambiguation by carrying out experiments using the English lexical sample data of SENSEVAL-2. We find that the gloss overlaps of Adapted Lesk and the semantic distance measure of Jiang and Conrath (1997) result in the highest accuracy.

Original languageEnglish (US)
Pages (from-to)241-257
Number of pages17
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2588
StatePublished - Dec 1 2003

Fingerprint Dive into the research topics of 'Using measures of semantic relatedness for word sense disambiguation'. Together they form a unique fingerprint.

Cite this