Inducing Terminology for Lexical Acquisition

Inducing Terminology for Lexical Acquisition Roberto Basili, Gianluca De Rossi, Maria Teresa Pazienza Department of Computer Science, Systems and Production University of Roma, Tor Vergata {basili ,derossi ,pazienza}@info .utovrm. it Abstract Few attention has been paid to terminol- ogy extraction for what concerns the pos- sibilities it offers to corpus linguistics and lexical acquisition. The problem of detect- ing terms in textual corpora has been ap- proached in a complex framework. Termi- nology is seen as the acquisition of domain specific knowledge (i.e. semantic features, selectional restrictions) for complex terms and /or unknown words. This has useful implications on more complex text process- ing tasks (e.g. information extraction). An hybrid symbolic and probabilistic approach to terminology extraction has been defined. The proposed inductive method puts a spe- cific attention to the linguistic description of what terms are as well as to the statis- tical characterization of terms as complex units of information typical of domain sub-. languages. Experimental evidence of the proposed method are discussed. 1 Introduction Nowadays corpus processing techniques are widely adopted to approach the well-known lexical bot- tleneck problems in language engineering. Lexical acquisition methods rely on collocational analysis (pure statistics), robust parsing (syntax-driven ac- quisition) or semantic annotations as they are found in large thesaura or on-line dictionaries. The lexical information that trigger induction varies from simple word/tokens to syntactically annotated or semanti- cally typed collocations (e.g. powerful vs. strong tea (Smadj a, 1989)), syntactic disambiguation rules (e.g. (Hindle and Rooths,1993), (Brill and Resnik,1994)) or sense disalnbiguation rules are usually derived. Such information is lexical as it, encodes constraints (of different types) at the word level, to be thus in- herited by morphologic variants of a given lemma. This strongly lexicalized knowledge, as it is ex- tracted from corpus data, requires lexical entries to be known in advance in some morphologic database. POS taggers or temmatizers are generally used to suitably map tokens to lemmas. It should be noted that lemmas in a corpus depends on the underly- ing sublanguage and their nature and shape is not as general as it is usually encoded in a morphologic dictionary. As an example, let studio (i.e. study as a noun) be an entry in an italian morphologic dic- tionary. Typical information in such a database is the following: studio pos=noun gen=mas aura=sing Tlle only legal morphologic variant of ,studio is studi (studies, with nura=plur). When searching for studio in a corpus of environment related texts 1, we found this kind of occurrences (e.g. short contexts): ... studi di base .... (basic studies) ... studi di impatto ambientale .... (*studies on the environmental impact) ... studi di fattibilitd .... (feasibility studies), ... studi di riferimento .... (reference studies) It is very common in a corpus (not balanced, thus focused to a limited domain) to find a set of specifi- cations of nouns that have some specific properties: • they are not always compositional (e.g. studio di base); * tlmy describe complex concepts (e.g. studi di fattibilitd) in the underlying …

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