Syntagmatic and Paradigmatic Representations of Term Variation

Syntagmatic and Paradigmatic Representations of Term Variation Christian Jacquemin LIMSI-CNRS BP 133 91403 ORSAY Cedex FRANCE j acquemin@limsi, fr Abstract A two-tier model for the description of morphologi- cal, syntactic and semantic variations of multi-word terms is presented. It is applied to term normal- ization of French and English corpora in the medi- cal and agricultural domains. Five different sources of morphological and semantic knowledge are ex- ploited (MULTEXT, CELEX, AGROVOC, Word- Netl.6, and Microsoft Word97 thesaurus). 1 Introduction In the classical approach to text retrieval, terms are assigned to queries and documents. The terms are generated by a process called automatic index- ing. Then, given a query, the similarity between the query and the documents is computed and a ranked list of documents is produced as output of the system for information access (Salton and McGill, 1983). The similarity between queries and documents de- pends on the terms they have in common. The same concept can be formulated in many different ways, known as variants, which should be conflated in order to avoid missing relevant documents. For this purpose, this paper proposes a novel model of term variation that integrates linguistic knowledge and performs accurate term normalization. It re- lies on previous or ongoing linguistic studies on this topic (Sparck Jones and Tait, 1984; Jacquemin et al., 1997; Hamon et al., 1998). Terms are described in a two-tier framework composed of a paradigmatic level and a syntagmatic level that account for the three linguistic dimensions of term variability (mor- phology, syntax, and semantics). Term variants are extracted from tagged corpora through FASTR 1, a unification-based transformational parser described in (Jacquemin et al., 1997). Four experiments are performed on the French and the English languages and a measure of pre- cision is provided for each of them. Two experi- ments are made on a French corpus [AGRIC] com- posed of 1.2 x 106 words of scientific abstracts in I FASTR can be downloaded www. limsi, f r/Individu/j acquemi/FASTR. from the agricultural domain and two on an English cor- pus [MEDIC] composed of 1.3 x 106 words of sci- entific abstracts in the medical domain. The two experiments in the French language are [AGRIC] + Word97 and [AGRIC] + AGROVOC. In the for- mer, synonymy links are extracted from the Mi- crosoft Word97 thesaurus; in the latter, seman- tic classes are extracted from the AGROVOC the- saurus, a thesaurus specialized in the agricultural domain (AGROVOC, 1995). In both experiments, morphological data are produced by a stemming al- gorithm applied to the MULTEXT lexical database (MULTEXT, 1998). The two experiments on the English language are [MEDIC] + WordNet 1.6 or [MEDIC] + Word97; they correspond to two differ- ent sources of semantic knowledge. In both cases, the morphological data are extracted from CELEX (CELEX, 1998). 2 Term Variation: Representation and Exploitation Terms and variations are represented into two par- allel frameworks illustrated by Figure 1. While terms are described by a unique pair composed of a structure--at the syntagmatic level--and a set …

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