A Symbolic and Surgical Acquisition of Terms through Variation

dc.creatorJacquemin, Christian
dc.date1995-05-04
dc.date1995-05-04
dc.date.accessioned2026-07-07T08:58:41Z
dc.date.available2026-07-07T08:58:41Z
dc.descriptionTerminological acquisition is an important issue in learning for NLP due to the constant terminological renewal through technological changes. Terms play a key role in several NLP-activities such as machine translation, automatic indexing or text understanding. In opposition to classical once-and-for-all approaches, we propose an incremental process for terminological enrichment which operates on existing reference lists and large corpora. Candidate terms are acquired by extracting variants of reference terms through {\em FASTR}, a unification-based partial parser. As acquisition is performed within specific morpho-syntactic contexts (coordinations, insertions or permutations of compounds), rich conceptual links are learned together with candidate terms. A clustering of terms related through coordination yields classes of conceptually close terms while graphs resulting from insertions denote generic/specific relations. A graceful degradation of the volume of acquisition on partial initial lists confirms the robustness of the method to incomplete data.
dc.description8 pages compressed uuencoded latex, uses aaai.sty, 1 figure .eps To appear in Proceedings Workshop "New Approaches to Learning for NLP" at IJCAI'95
dc.identifierhttps://arxiv.org/abs/cmp-lg/9505012
dc.identifierhttp://arxiv.org/abs/cmp-lg/9505012
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/147422
dc.subjectComputation and Language
dc.titleA Symbolic and Surgical Acquisition of Terms through Variation
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