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Communication Dans Un Congrès Année : 2020

TermEval 2020: TALN-LS2N System for Automatic Term Extraction

Résumé

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.
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Dates et versions

hal-03272824 , version 1 (28-06-2021)

Identifiants

  • HAL Id : hal-03272824 , version 1

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Amir Hazem, Mérième Bouhandi, Florian Boudin, Béatrice Daille. TermEval 2020: TALN-LS2N System for Automatic Term Extraction. 6th International Workshop on Computational Terminology (COMPUTERM 2020), May 2020, Marseille, France. ⟨hal-03272824⟩
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