| 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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| Creator(s): | |
| Creation Date: | September 2007 |
| Partner(s): |
UNT College of Engineering
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| Collection(s): |
UNT Scholarly Works
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| Usage: |
Total Uses: 47
Past 30 days: 0
Yesterday: 0
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| Creator (Author): |
Sinha, Ravi
University of North Texas |
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| Creator (Author): |
Mihalcea, Rada
University of North Texas |
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| Publisher Info: |
Publisher Name: Institute of Electrical and Electronics Engineers (IEEE)
Place of Publication: [New York, New York]
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| Original Creation Date: | 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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| Degree: |
Department:
Computer Science and Engineering
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| Physical Description: |
7 p. |
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| 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): |
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| Partner: |
UNT College of Engineering
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| Collection: |
UNT Scholarly Works
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| Resource Type: | Paper | |
| Format: | Text | |
| Rights: |
Access:
Public
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