TermEval 2020: TALN-LS2N System for Automatic Term Extraction
Amir Hazem, Mérieme Bouhandi, Florian Boudin, Beatrice Daille
Abstract
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.- Anthology ID:
- 2020.computerm-1.13
- Volume:
- Proceedings of the 6th International Workshop on Computational Terminology
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Béatrice Daille, Kyo Kageura, Ayla Rigouts Terryn
- Venue:
- CompuTerm
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 95–100
- Language:
- English
- URL:
- https://aclanthology.org/2020.computerm-1.13
- DOI:
- Cite (ACL):
- Amir Hazem, Mérieme Bouhandi, Florian Boudin, and Beatrice Daille. 2020. TermEval 2020: TALN-LS2N System for Automatic Term Extraction. In Proceedings of the 6th International Workshop on Computational Terminology, pages 95–100, Marseille, France. European Language Resources Association.
- Cite (Informal):
- TermEval 2020: TALN-LS2N System for Automatic Term Extraction (Hazem et al., CompuTerm 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.computerm-1.13.pdf