UMND1: Unsupervised word sense disambiguation using contextual semantic relatedness

Siddharth Patwardhan, Satanjeev Banerjee, Ted Pedersen

Research output: Contribution to conferencePaperpeer-review

31 Scopus citations

Abstract

In this paper we describe an unsupervised WordNet-based Word Sense Disambiguation system, which participated (as UMND1) in the SemEval-2007 Coarsegrained English Lexical Sample task. The system disambiguates a target word by using WordNet-based measures of semantic relatedness to find the sense of the word that is semantically most strongly related to the senses of the words in the context of the target word. We briefly describe this system, the configuration options used for the task, and present some analysis of the results.

Original languageEnglish (US)
Pages390-393
Number of pages4
StatePublished - 2007
Externally publishedYes
Event4th International Workshop on Semantic Evaluations, SemEval 2007 - Prague, Czech Republic
Duration: Jun 23 2007Jun 24 2007

Other

Other4th International Workshop on Semantic Evaluations, SemEval 2007
CountryCzech Republic
CityPrague
Period6/23/076/24/07

Bibliographical note

Funding Information:
This research was partially supported by a National Science Foundation Early CAREER Development award (#0092784).

Publisher Copyright:
© 2007 Association for Computational Linguistics.

Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.

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