Unsupervised Graph-based Word Sense Disambiguation Using Measures of Word Semantic Similarity

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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.

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7 p.

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Sinha, Ravi & Mihalcea, Rada, 1974- September 2007.

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This paper is part of the collection entitled: UNT Scholarly Works and was provided by the UNT College of Engineering to the UNT Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 376 times, with 5 in the last month. More information about this paper can be viewed below.

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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.

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7 p.

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  • Institute of Electrical and Electronics Engineers (IEEE) International Conference on Semantic Computing (ICSC), September 2007. Irvine, CA, United States

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  • September 2007

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  • Jan. 31, 2011, 2:01 p.m.

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  • Dec. 4, 2023, 1:15 p.m.

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Sinha, Ravi & Mihalcea, Rada, 1974-. Unsupervised Graph-based Word Sense Disambiguation Using Measures of Word Semantic Similarity, paper, September 2007; [New York, New York]. (https://digital.library.unt.edu/ark:/67531/metadc30999/: accessed June 20, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT College of Engineering.

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