DocumentCode
3466660
Title
Unsupervised Graph-basedWord Sense Disambiguation Using Measures of Word Semantic Similarity
Author
Sinha, Ravi ; Mihalcea, Rada
Author_Institution
Univ. of North Texas, Denton
fYear
2007
fDate
17-19 Sept. 2007
Firstpage
363
Lastpage
369
Abstract
This paper describes an unsupervised graph-based method for word sense disambiguation, and presents comparative evaluations using several measures of word semantic similarity and several algorithms for graph centrality. The results indicate that the right combination of similarity metrics and graph centrality algorithms can lead to a performance competing with the state-of-the-art in unsupervised word sense disambiguation, as measured on standard data sets.
Keywords
computational linguistics; graph theory; natural language processing; graph centrality; unsupervised graph-based method; word semantic; word sense disambiguation; Computer science; Encoding; Humans; Labeling; Measurement standards; Natural languages; Production facilities;
fLanguage
English
Publisher
ieee
Conference_Titel
Semantic Computing, 2007. ICSC 2007. International Conference on
Conference_Location
Irvine, CA
Print_ISBN
978-0-7695-2997-4
Type
conf
DOI
10.1109/ICSC.2007.87
Filename
4338370
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