Exploring Concepts' Semantic Relations for Clustering-Based Query Senses Disambiguation

摘要

For most Web searching applications, queries are commonly ambiguous because words usually contain several senses. Traditional Word Sense Disambiguation (WSD) methods use statistic models or ontology-based knowledge models to find the most appropriate sense for the ambiguous word. Since queries are usually short and may not provide enough context information for disambiguating queries, more than one appropriate interpretation for ambiguous queries may be found. Thus, it is not always reasonable for finding only one interpretation of the query. In this paper, we propose a cluster-based WSD method, which finds out all appropriate interpretations for the query. Because some senses of one ambiguous word usually have very close semantic relations, we may group those similar senses together for explaining the ambiguous word in one interpretation.