Automatic acquisition of sense examples using Exretriever

Abstract

A current research line for word sense disambiguation (WSD) focuses on the use of supervised machine learning techniques. One of the drawbacks of using such techniques is that previously sense annotated data is required. This paper presents ExRetriever, a new software tool for automatically acquiring large sets of sense tagged examples from large collections of text and the Web. ExRetriever exploits the knowledge contained in large-scale knowledge bases (e.g., WordNet) to build complex queries, each of them characterising particular senses of a word. These examples can be used as training instances for supervised WSD algorithms.