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.
Date:
September 2007
Creator:
Sinha, Ravi & Mihalcea, Rada, 1974-
Partner:
UNT College of Engineering