Automatic Adaptation of WordNet to Sublanguages and to Computational Tasks

Semantically tagging a corpus is useful for many intermediate NLP tasks such as: acquisition of word argument structures in sublanguages, acquisition of syntactic disambiguation cues, terminology learning, etc. Semantic categories allow the generalization of observed word patterns, and facilitate the discovery of irecurrent sublanguage phenomena and selectional rules of various types. Yet, as opposed to POS tags in morphology, there is no consensus in literature about the type and granularity of the category inventory. In addition, most available on-line taxonomies, as WordNet, are over ambiguous and, at the same time, may not include many domain-dependent senses of words. In this paper we describe a method to adapt a general purpose taxonomy to an application sub[anguage: flint, we prune branches of the Wordnet hierarchy that are too " fine grained" for the domain: then. a statistical model of classes is built from corpus contexts to sort the different classifications or assign a classification to known and unknown words, respectively. 1 I n t r o d u c t i o n Lexical learning methods based on the use of semantic categories are faced with the problem of overambiguity and entangled structures of Thesaura and dictionaries. WordNet and Roget's Thesaura were not initially conceived, despite their success among researchers in lexical statistics, as tools for automatic language processing. The purpose was rather to provide the linguists with a very refined, general purpose, linguistically motivated source of taxonomic knowledge. As a consequence, in most on-fine Thesaura words are extremely ambiguous. with very subtle distinctions among senses. High ambiguity, entangled nodes, and asymmetry have already been emphasized in (Hearst and Shutze, 1993) as being an obstacle to the effective use of on-line Thesaura in corpus linguistics. In most cases, the noise introduced by overambiguity almost overrides the positive effect of semantic clustering. For example, in (BriIl and Resnik, 1994) clustering PP heads according to WordNet synsets produced only a [% improvement in a PP disambiguation task. with respect to the non-clustered method. A subsequent paper (Resnik. 1997) reports of a 40% precision in a sense disambiguation task. always based on generalization through WordNet synsets. Context-based sense clisambiguation becomes a prohibitive task on a wide-scale basis, because when words in the context of unambiguous word are replaced by their s.vnsets, there is a multiplication of possible contexts, rather than a generalization. [n (Agirre and Rigau. 1996) a method called Conceptual Distance is proposed to reduce this problem, but the reported performance in disambiguation still does not reach 50%. On the other hand, (Dolan. 1994) and (Krovetz and Croft. 1992) claim that fine-grained semantic distinctions are unlikely to be of practical value for many applications. Our experience supports this claim: often, what matters is to be able to distinguish among contrastive (Pustejowsky. 1995) ambiguities of the bank_river bank_organisation flavor. The problem however is that the notion of"coutrast ive" is domain-dependent. Depending upon the sublanguage (e.g. medicine, finance, computers. etc.) and upon the specific NLP application (e.g. Information Extraction, Dialogue etc.) a given semantic label may be too general or too specific for the task at hand. For example, the word line has 27 senses in WordNet. many of which draw subtle distinctions e.g. line of ~cork (sense 26) and line of products (sense [9). In aa