TermEval 2020: TALN-LS2N System for Automatic Term Extraction

Amir Hazem (1) , Mérième Bouhandi (1) , Florian Boudin (1) , Béatrice Daille (1)

1 LS2N - équipe TALN - Traitement Automatique du Langage Naturel

Automatic terminology extraction is a notoriously difficult task aiming to ease effort demanded to manually identify terms in domain-specific corpora by automatically providing a ranked list of candidate terms. The main ways that addressed this task can be ranged in four main categories: (i) rule-based approaches, (ii) feature-based approaches, (iii) context-based approaches, and (iv) hybrid approaches. For this first TermEval shared task, we explore a feature-based approach, and a deep neural network multitask approach-BERT-that we fine-tune for term extraction. We show that BERT models (RoBERTa for English and CamemBERT for French) outperform other systems for French and English languages.

Mots clés: Terminology extraction; Feature-based; BERT

Domaines: Recherche d'information [cs.IR]; Traitement du texte et du document

Amir Hazem, Mérième Bouhandi, Florian Boudin, Béatrice Daille. TermEval 2020: TALN-LS2N System for Automatic Term Extraction. International Workshop on Computational Terminology (COMPUTERM), May 2020, Marseille, France. ⟨hal-03477769⟩