A Symbolic and Surgical Acquisition of Terms through Variation
Christian Jacquemin (Institut de Recherches en Informatique de Nantes)

TL;DR
This paper presents an incremental method for terminological acquisition in NLP that extracts and clusters term variants from large corpora using a unification-based parser, enriching reference lists with conceptually linked terms.
Contribution
It introduces a novel approach combining variant extraction, contextual analysis, and clustering to improve terminological enrichment in NLP systems.
Findings
Robustness to incomplete initial lists
Effective clustering of related terms
Successful extraction of conceptually linked terms
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNatural Language Processing Techniques · linguistics and terminology studies · Translation Studies and Practices
