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

Description:

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.

Creator(s):
Creation Date: September 2007
Partner(s):
UNT College of Engineering
Collection(s):
UNT Scholarly Works
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Total Uses: 118
Past 30 days: 8
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Creator (Author):
Sinha, Ravi

University of North Texas

Creator (Author):
Mihalcea, Rada, 1974-

University of North Texas

Publisher Info:
Place of Publication: [New York, New York]
Date(s):
  • Creation: September 2007
Description:

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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Physical Description:

7 p.

Language(s):
Subject(s):
Keyword(s): word sense disambiguation | semantic similarities | SENSEVAL
Source: Institute of Electrical and Electronics Engineers (IEEE) International Conference on Semantic Computing (ICSC), 2007, Irvine, California, United States
Contributor(s):
Partner:
UNT College of Engineering
Collection:
UNT Scholarly Works
Identifier:
  • ARK: ark:/67531/metadc30999
Resource Type: Paper
Format: Text
Rights:
Access: Public