<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="gharbieh-etal-2017-deep">
    <titleInfo>
        <title>Deep Learning Models For Multiword Expression Identification</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Waseem</namePart>
        <namePart type="family">Gharbieh</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Virendrakumar</namePart>
        <namePart type="family">Bhavsar</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Paul</namePart>
        <namePart type="family">Cook</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Nancy</namePart>
            <namePart type="family">Ide</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Aurélie</namePart>
            <namePart type="family">Herbelot</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Lluís</namePart>
            <namePart type="family">Màrquez</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Vancouver, Canada</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Multiword expressions (MWEs) are lexical items that can be decomposed into multiple component words, but have properties that are unpredictable with respect to their component words. In this paper we propose the first deep learning models for token-level identification of MWEs. Specifically, we consider a layered feedforward network, a recurrent neural network, and convolutional neural networks. In experimental results we show that convolutional neural networks are able to outperform the previous state-of-the-art for MWE identification, with a convolutional neural network with three hidden layers giving the best performance.</abstract>
    <identifier type="citekey">gharbieh-etal-2017-deep</identifier>
    <identifier type="doi">10.18653/v1/S17-1006</identifier>
    <location>
        <url>https://aclanthology.org/S17-1006</url>
    </location>
    <part>
        <date>2017-08</date>
        <extent unit="page">
            <start>54</start>
            <end>64</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Deep Learning Models For Multiword Expression Identification
%A Gharbieh, Waseem
%A Bhavsar, Virendrakumar
%A Cook, Paul
%Y Ide, Nancy
%Y Herbelot, Aurélie
%Y Màrquez, Lluís
%S Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F gharbieh-etal-2017-deep
%X Multiword expressions (MWEs) are lexical items that can be decomposed into multiple component words, but have properties that are unpredictable with respect to their component words. In this paper we propose the first deep learning models for token-level identification of MWEs. Specifically, we consider a layered feedforward network, a recurrent neural network, and convolutional neural networks. In experimental results we show that convolutional neural networks are able to outperform the previous state-of-the-art for MWE identification, with a convolutional neural network with three hidden layers giving the best performance.
%R 10.18653/v1/S17-1006
%U https://aclanthology.org/S17-1006
%U https://doi.org/10.18653/v1/S17-1006
%P 54-64

Markdown (Informal)

[Deep Learning Models For Multiword Expression Identification](https://aclanthology.org/S17-1006) (Gharbieh et al., *SEM 2017)

• Deep Learning Models For Multiword Expression Identification (Gharbieh et al., *SEM 2017)

ACL

• Waseem Gharbieh, Virendrakumar Bhavsar, and Paul Cook. 2017. Deep Learning Models For Multiword Expression Identification. In Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017), pages 54–64, Vancouver, Canada. Association for Computational Linguistics.