A Symbolic and Surgical Acquisition of Terms through Variation

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 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.