Paper: Inducing Word Senses to Improve Web Search Result Clustering

ACL ID D10-1012
Title Inducing Word Senses to Improve Web Search Result Clustering
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2010
Authors

In this paper, we present a novel approach to Web search result clustering based on the au- tomatic discovery of word senses from raw text, a task referred to as Word Sense Induc- tion (WSI). We first acquire the senses (i.e., meanings) of a query by means of a graph- based clustering algorithm that exploits cycles (triangles and squares) in the co-occurrence graph of the query. Then we cluster the search results based on their semantic similarity to the induced word senses. Our experiments, conducted on datasets of ambiguous queries, show that our approach improves search result clustering in terms of both clustering quality and degree of diversification.