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        <title>Verbal Multiword Expression Identification: Do We Need a Sledgehammer to Crack a Nut?</title>
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    <name type="personal">
        <namePart type="given">Caroline</namePart>
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    <name type="personal">
        <namePart type="given">Agata</namePart>
        <namePart type="family">Savary</namePart>
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    <name type="personal">
        <namePart type="given">Carlos</namePart>
        <namePart type="family">Ramisch</namePart>
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    <name type="personal">
        <namePart type="given">Jean-Yves</namePart>
        <namePart type="family">Antoine</namePart>
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            <title>Proceedings of the 28th International Conference on Computational Linguistics</title>
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            <namePart type="given">Donia</namePart>
            <namePart type="family">Scott</namePart>
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        <name type="personal">
            <namePart type="given">Nuria</namePart>
            <namePart type="family">Bel</namePart>
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        <name type="personal">
            <namePart type="given">Chengqing</namePart>
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    <abstract>Automatic identification of multiword expressions (MWEs), like ‘to cut corners’ (to do an incomplete job), is a pre-requisite for semantically-oriented downstream applications. This task is challenging because MWEs, especially verbal ones (VMWEs), exhibit surface variability. This paper deals with a subproblem of VMWE identification: the identification of occurrences of previously seen VMWEs. A simple language-independent system based on a combination of filters competes with the best systems from a recent shared task: it obtains the best averaged F-score over 11 languages (0.6653) and even the best score for both seen and unseen VMWEs due to the high proportion of seen VMWEs in texts. This highlights the fact that focusing on the identification of seen VMWEs could be a strategy to improve VMWE identification in general.</abstract>
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%0 Conference Proceedings
%T Verbal Multiword Expression Identification: Do We Need a Sledgehammer to Crack a Nut?
%A Pasquer, Caroline
%A Savary, Agata
%A Ramisch, Carlos
%A Antoine, Jean-Yves
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F pasquer-etal-2020-verbal
%X Automatic identification of multiword expressions (MWEs), like ‘to cut corners’ (to do an incomplete job), is a pre-requisite for semantically-oriented downstream applications. This task is challenging because MWEs, especially verbal ones (VMWEs), exhibit surface variability. This paper deals with a subproblem of VMWE identification: the identification of occurrences of previously seen VMWEs. A simple language-independent system based on a combination of filters competes with the best systems from a recent shared task: it obtains the best averaged F-score over 11 languages (0.6653) and even the best score for both seen and unseen VMWEs due to the high proportion of seen VMWEs in texts. This highlights the fact that focusing on the identification of seen VMWEs could be a strategy to improve VMWE identification in general.
%R 10.18653/v1/2020.coling-main.296
%U https://aclanthology.org/2020.coling-main.296
%U https://doi.org/10.18653/v1/2020.coling-main.296
%P 3333-3345

Markdown (Informal)

[Verbal Multiword Expression Identification: Do We Need a Sledgehammer to Crack a Nut?](https://aclanthology.org/2020.coling-main.296) (Pasquer et al., COLING 2020)

• Verbal Multiword Expression Identification: Do We Need a Sledgehammer to Crack a Nut? (Pasquer et al., COLING 2020)

ACL

• Caroline Pasquer, Agata Savary, Carlos Ramisch, and Jean-Yves Antoine. 2020. Verbal Multiword Expression Identification: Do We Need a Sledgehammer to Crack a Nut?. In Proceedings of the 28th International Conference on Computational Linguistics, pages 3333–3345, Barcelona, Spain (Online). International Committee on Computational Linguistics.