document: Discovering word senses from text Inventories of manually compiled dictionaries usually serve as a source for word senses . However , they often include many rare senses while missing corpus\/domain-specific senses . We present a clustering algorithm called CBC ( Clustering By Committee ) that automatically discovers word senses from text . It initially discovers a set of tight clusters called committees that are well scattered in the similarity space . The centroid of the members of a committee is used as the feature vector of the cluster . We proceed by assigning words to their most similar clusters . After assigning an element to a cluster , we remove their overlapping features from the element . This allows CBC to discover the less frequent senses of a word and to avoid discovering duplicate senses . Each cluster that a word belongs to represents one of its senses . We also present an evaluation methodology for automatically measuring the precision and recall of discovered senses .
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