Term Extraction + Term Clustering: An Integrated Platform for Computer-Aided Terminology

A novel technique for automatic thesaurus construction is proposed. It is based on the complementary use of two tools: (1) a Term Extraction tool that acquires term candidates from tagged corpora through a shallow grammar of noun phrases, and (2) a Term Clustering tool that groups syntactic variants (insertions). Experiments performed on corpora in three technical domains yield clusters of term candidates with precision rates between 93% and 98%. 1 C o m p u t a t i o n a l T e r m i n o l o g y In the domain of corpus-based terminology two types of tools are currently developed: tools for automatic term extraction (Bourigault, 1993; Justeson and Katz, 1995; Daille, 1996; Brun, 1998) and tools for automatic thesaurus construction (Grefenstette, 1994). These tools are expected to be complementary in the sense that the links and clusters proposed in automatic thesaurus construction can be exploited for structuring the term candidates produced by the automatic term extractors. In fact, complementarity is difficult because term extractors provide mainly multi-word terms, while tools for automatic thesaurus construction yield clusters of single-word terms. On the one hand, term extractors focus on multi-word terms for ontological motivations: single-word terms are too polysemous and too generic and it is therefore necessary to provide the user with multi-word terms that represent finer concepts in a domain. The counterpart of this focus is that automatic term extractors yield important volumes of data that require structuring through a postprocessor. On the other hand, tools for automatic thesaurus construction focus on single-word terms for practical reasons. Since they cluster terms through statistical measures of context similarities, these tools exploit recurring situations. Since single-word terms denote broader concepts than multi-word terms, they appear more frequently in corpora and are therefore more appropriate for statistical clustering. The contribution of this paper is to propose an integrated platform for computer-aided term extraction and structuring that results from the combination of LEXTER, a Term Extraction tool (Bouriganlt et al., 1996), and FASTR 1, a Term Normalization tool (Jacquemin et al., 1997). 2 Components of the Platform for Computer-Aided Terminology The platform for computer-aided terminology is organized as a chain of four modules and the corresponding flowchart is given by Figure 1. The modules are: P O S t agg ing First the corpus is processed by Sylex, a Part-of-Speech tagger. Each word is unambiguously tagged and receives a single lemma. T e r m E x t r a c t i o n LEXTER, the term extraction tool acquires term candidates from the tagged corpus. In a first step, LEXTER exploits the part-of-speech categories for extracting maximal-length noun phrases. It relies on makers of frontiers together with a shallow grammar of noun phrases. In a second step, LEXTER recursively decomposes these maximal-length noun phrases into two syntactic constituents (Head and Expansion). T e r m C l u s t e r i n g The term clustering tool groups the term candidates produced at the * FA STR can be downloaded www. limsi, fr/Individu/j acquemi/FASTK. from