A Symbolic and Surgical Acquisition of Terms through Variation
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.