  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.
