Unsupervised graph-based word sense disambiguation using measures of word semantic similarity

被引：97
|
作者：
Sinha, Ravi [1 ]
Mihalcea, Rada [1 ]
机构：
[1] Univ North Texas, Dept Comp Sci & Engn, Denton, TX 76203 USA
来源：
ICSC 2007: INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING, PROCEEDINGS | 2007年
关键词：
D O I：
10.1109/ICSC.2007.87
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
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.
引用
收藏
页码：363 / +
页数：2
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