1995/05/04 by Christian Jacquemin, Jacquemin, Christian
Arts and Humanities · Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Translation Studies and Practices #cmp-lg #cs.CL #linguistics and terminology studies
paper · pdf · doi:10.48550/arxiv.cmp-lg/9505012
8 pages compressed uuencoded latex, uses aaai.sty, 1 figure .eps To appear in Proceedings Workshop "New Approaches to Learning for NLP" at IJCAI'95
arxiv created 1995/05/04 · openalex publication_date 1995/05/04 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Terminological 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.