Hierarchical Clustering Of Verbs

In this paper we present an unsupervised learning algorithm for incremental concept formation, based on an augmented version of COBWEB. The algorithm is applied to the task of acquiring a verb taxonomy through the systematic observation of verb usages in corpora. Using a Machine Learning methodology for a Natural language problem required adjustments on both sides. In fact, concept formation algorithms assume the input information as being stable, unambiguous and complete. At the opposite, linguistic data are ambiguous, incomplete, and possibly erroneous. A NL processor is used to extract semiautomatically from corpora the thematic roles of verbs and derive a feature-vector representation of verb instances. In order to account for multiple instances of the same verb, the measure of category utility, defined in COBWEB, has been augmented with the notion of memory inertia. Memory inertia models the influence that previously classified instances of a given verb have on the classification of subsequent instances of the same verb. Finally, a method is defined to identify the basic-level classes of an acquired hierarchy, i.e. those bringing the most predictive information about their members.