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        <title>TermEval 2020: TALN-LS2N System for Automatic Term Extraction</title>
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    <name type="personal">
        <namePart type="given">Amir</namePart>
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    <name type="personal">
        <namePart type="given">Mérieme</namePart>
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    <name type="personal">
        <namePart type="given">Florian</namePart>
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    <name type="personal">
        <namePart type="given">Beatrice</namePart>
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            <title>Proceedings of the 6th International Workshop on Computational Terminology</title>
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            <namePart type="given">Kyo</namePart>
            <namePart type="family">Kageura</namePart>
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        <name type="personal">
            <namePart type="given">Ayla</namePart>
            <namePart type="given">Rigouts</namePart>
            <namePart type="family">Terryn</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T TermEval 2020: TALN-LS2N System for Automatic Term Extraction
%A Hazem, Amir
%A Bouhandi, Mérieme
%A Boudin, Florian
%A Daille, Beatrice
%Y Daille, Béatrice
%Y Kageura, Kyo
%Y Terryn, Ayla Rigouts
%S Proceedings of the 6th International Workshop on Computational Terminology
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-57-3
%G eng
%F hazem-etal-2020-termeval
%X 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.
%U https://aclanthology.org/2020.computerm-1.13/
%P 95-100

Markdown (Informal)

[TermEval 2020: TALN-LS2N System for Automatic Term Extraction](https://aclanthology.org/2020.computerm-1.13/) (Hazem et al., CompuTerm 2020)

• TermEval 2020: TALN-LS2N System for Automatic Term Extraction (Hazem et al., CompuTerm 2020)

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.