Semantic document engineering with WordNet and PageRank

Authors:
Paul Tarau;Rada Mihalcea;Elizabeth Figa
Affiliations:
University of North Texas, Denton, Texas;University of North Texas, Denton, Texas;University of North, Denton, Texas
Venue:
Proceedings of the 2005 ACM symposium on Applied computing
Year:
2005

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Abstract

This paper describes Natural Language Processing techniques for document engineering in combination with graph algorithms and statistical methods. Google's PageRank and similar fast-converging recursive graph algorithms have provided practical means to statically rank vertices of large graphs like the World Wide Web. By combining a fast Java-based PageRank implementation with a Prolog base inferential layer, running on top of an optimized WordNet graph, we describe applications to word sense disambiguation and evaluate their accuracy on standard benchmarks.