Clustering of Polysemic Words

In this paper, we propose an approach for constructing clusters of related terms that may be used for deriving formal conceptual structures in a later stage. In contrast to previous approaches in this direction, we explicitly take into account the fact that words can have different, possibly even unrelated, meanings. To account for such ambiguities in word meaning, we consider two alternative soft clustering techniques, namely Overlapping Pole-Based Clustering (PoBOC) and Clustering by Committees (CBC). These soft clustering algorithms are used to detect different contexts of the clustered words, resulting in possibly more than one cluster membership per word. We report on initial experiments conducted on textual data from the tourism domain.