Hierarchical Clustering of Verbs

HIERARCHICAL CLUSTERING OF VERBS Roberto Basili (*) Maria Teresa Pazienza (*) Paola Velardi (**) (*) Universita' di Roma T~x Vergata, Italy (**) Universita' di Ancona, Italy Abstract. 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 semi- automatically 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. 1. Introduction The design of word-sense taxonornies is acknowledged as one of the most difficult (and frustrating) tasks in NLP systems. The decision to assign a word to a category is far from being straightforward (Nirenburg and Raskin (1987)) and often the lexicon builders do not use consistent classification pfincipia. Automatic approaches to the acquisition of word taxonomies have generally made use of machine readable dictionaries (MRD), for the typical definitory nature of MRD texts. For example, in Byrd et al., (1987) and other similar studies the category of a word is acquired from the first few words of a dictionary definition. Besides the well known problems of inconsistency and circularity of definitions, an inherent difficulty with this approach is that verbs can hardly be defined in terms of genus and differentiae. Verb semantics resides in the nature of the event they describe, that is better expressed by the roles played by its arguments in a sentence. Psycholinguistie studies on verb semantics outline the relevance of thematic roles, especially in eategorisation activities Keil, (1989), Jackendoff (1983) and indicate the argument structure of verbs as playing a central role in language acquisition Pinker (1989). In NLP, representing verb semantics with their thematic roles is a consolidated practice, even though theoretical researches (Pustejovski (1991)) propose more rich and formal representation frameworks. More recent papers Hindle (1990), Pereira and Tishby (1992) proposed to cluster nouns on the basis of a metric derived from the distribution of subject, verb and object in the texts. Both papers use as a source of information large corpora, but differ in the type of statistical approach used to determine word similarity. These studies, though valuable, leave several open problems: 70 1) A metric of conceptual closeness based on mere …

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