Computer Aided Interpretation of Lexical Cooccurrences

COMPUTER AIDED INTERPRETATION OF LEXICAL COOCCURRENCES Paola Velardi (*) Mafia Teresa Pazienza (**) (*)University of Ancona, Istituto di Informatica, via Brecce Bianche, Ancona (**)University of Roma, Dip. di lnformatica e Sistemistica, via Buonarroti 12, Roma ABSTRACT This paper addresses the problem of developing a large semantic lexicon for natural language processing. The increas~g availability of machine readable documents offers an opportunity to the field of lexieal semantics, by providing experimental evidence of word uses (on-line texts) and word definitions (on-line dictionaries). The system presented hereafter, PETRARCA, detects word e.occurrences from a large sample of press agency releases on finance and economics, and uses these associations to build a ease-based semantic lexicon. Syntactically valid cooccurenees including a new word W are detected by a high-coverage morphosyntactic analyzer. Syntactic relations are interpreted e,g. replaced by case relations, using a a catalogue of patterns/interpretation pairs, a concept type hierarchy, and a set of selectional restriction rules on semantic interpretation types. Introduction Semantic knowledge codification for language processing requires two important issues to be considered: 1. Meaning representation. Each word is a world: how can we conveniently circumscribe the semantic information associated to a lexic,;d entry? 2. Acquisition. For a language processor, to implement a useful application, several thousands of terms must have an entry in the semantic lexicon: how do we cope with one such a prohibitive task? 185 The problem of meaning representation is one which preoccupied scientists of different disciplines since the early history of human culture. We will not attempt an overall survey of the field of semantics, that provided material for many fascinating books; rather, we will concentrate On the computer science perspective, i.e. how do we go about representing language expressions on a computer, in a way that can be useful for natural language processing applications, e.g. machine translation, information retrieval, user-friendly interfaces. In the field of computational linguistics, several approaches were followed for representing semantic knowledge. We are not concerned here with semantic languages, which are relatively well developed; the diversity lies in the meaning representation principles. We will classify the methods of meaning representations in two categories: conceptual (or deep) and coilocative (or surface). The terms "conceptual" and "collocative" have been introduced in [81; we decided to adopt an existing terminology, even though our interpretation of the above two categories is broader than for their inventor. 1. Conceptual Meaning Conceptual meaning is the cognitive content of words; it can be expressed by features or by primitives. Conceptual meaning is "deep" in that it expresses phenomena that are deeply embedded in language. 2. Collocatlve meaning. What is communicated through associations between words or word classes. Coilocative meaning is "superficial" in that does not seek for "the deep sense" of a word, but rather it "describes" its uses in everyday language, or in some sub-w, rid language (economy, computers, etc.). It provides more than a simple analysis of cooccurr~aces, because it attempts an explanation of word associations in terms of conceptual relations between a lexical item and other …

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