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        <title>The Impact of Word Representations on Sequential Neural MWE Identification</title>
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    <name type="personal">
        <namePart type="given">Nicolas</namePart>
        <namePart type="family">Zampieri</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">Geraldine</namePart>
        <namePart type="family">Damnati</namePart>
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        <dateIssued>2019-08</dateIssued>
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            <title>Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)</title>
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        <name type="personal">
            <namePart type="given">Agata</namePart>
            <namePart type="family">Savary</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Carla</namePart>
            <namePart type="given">Parra</namePart>
            <namePart type="family">Escartín</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Francis</namePart>
            <namePart type="family">Bond</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Jelena</namePart>
            <namePart type="family">Mitrović</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Verginica</namePart>
            <namePart type="given">Barbu</namePart>
            <namePart type="family">Mititelu</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Recent initiatives such as the PARSEME shared task allowed the rapid development of MWE identification systems. Many of those are based on recent NLP advances, using neural sequence models that take continuous word representations as input. We study two related questions in neural MWE identification: (a) the use of lemmas and/or surface forms as input features, and (b) the use of word-based or character-based embeddings to represent them. Our experiments on Basque, French, and Polish show that character-based representations yield systematically better results than word-based ones. In some cases, character-based representations of surface forms can be used as a proxy for lemmas, depending on the morphological complexity of the language.</abstract>
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    <identifier type="doi">10.18653/v1/W19-5121</identifier>
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            <start>169</start>
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%0 Conference Proceedings
%T The Impact of Word Representations on Sequential Neural MWE Identification
%A Zampieri, Nicolas
%A Ramisch, Carlos
%A Damnati, Geraldine
%Y Savary, Agata
%Y Escartín, Carla Parra
%Y Bond, Francis
%Y Mitrović, Jelena
%Y Mititelu, Verginica Barbu
%S Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F zampieri-etal-2019-impact
%X Recent initiatives such as the PARSEME shared task allowed the rapid development of MWE identification systems. Many of those are based on recent NLP advances, using neural sequence models that take continuous word representations as input. We study two related questions in neural MWE identification: (a) the use of lemmas and/or surface forms as input features, and (b) the use of word-based or character-based embeddings to represent them. Our experiments on Basque, French, and Polish show that character-based representations yield systematically better results than word-based ones. In some cases, character-based representations of surface forms can be used as a proxy for lemmas, depending on the morphological complexity of the language.
%R 10.18653/v1/W19-5121
%U https://aclanthology.org/W19-5121
%U https://doi.org/10.18653/v1/W19-5121
%P 169-175

Markdown (Informal)

[The Impact of Word Representations on Sequential Neural MWE Identification](https://aclanthology.org/W19-5121) (Zampieri et al., MWE 2019)

• The Impact of Word Representations on Sequential Neural MWE Identification (Zampieri et al., MWE 2019)

ACL

• Nicolas Zampieri, Carlos Ramisch, and Geraldine Damnati. 2019. The Impact of Word Representations on Sequential Neural MWE Identification. In Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019), pages 169–175, Florence, Italy. Association for Computational Linguistics.