<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="swarnkar-singh-2018-di">
    <titleInfo>
        <title>Di-LSTM Contrast : A Deep Neural Network for Metaphor Detection</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Krishnkant</namePart>
        <namePart type="family">Swarnkar</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Anil</namePart>
        <namePart type="given">Kumar</namePart>
        <namePart type="family">Singh</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Workshop on Figurative Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Beata</namePart>
            <namePart type="family">Beigman Klebanov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Shutova</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Patricia</namePart>
            <namePart type="family">Lichtenstein</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Smaranda</namePart>
            <namePart type="family">Muresan</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Chee</namePart>
            <namePart type="family">Wee</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">New Orleans, Louisiana</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The contrast between the contextual and general meaning of a word serves as an important clue for detecting its metaphoricity. In this paper, we present a deep neural architecture for metaphor detection which exploits this contrast. Additionally, we also use cost-sensitive learning by re-weighting examples, and baseline features like concreteness ratings, POS and WordNet-based features. The best performing system of ours achieves an overall F1 score of 0.570 on All POS category and 0.605 on the Verbs category at the Metaphor Shared Task 2018.</abstract>
    <identifier type="citekey">swarnkar-singh-2018-di</identifier>
    <identifier type="doi">10.18653/v1/W18-0914</identifier>
    <location>
        <url>https://aclanthology.org/W18-0914</url>
    </location>
    <part>
        <date>2018-06</date>
        <extent unit="page">
            <start>115</start>
            <end>120</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Di-LSTM Contrast : A Deep Neural Network for Metaphor Detection
%A Swarnkar, Krishnkant
%A Singh, Anil Kumar
%Y Beigman Klebanov, Beata
%Y Shutova, Ekaterina
%Y Lichtenstein, Patricia
%Y Muresan, Smaranda
%Y Wee, Chee
%S Proceedings of the Workshop on Figurative Language Processing
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F swarnkar-singh-2018-di
%X The contrast between the contextual and general meaning of a word serves as an important clue for detecting its metaphoricity. In this paper, we present a deep neural architecture for metaphor detection which exploits this contrast. Additionally, we also use cost-sensitive learning by re-weighting examples, and baseline features like concreteness ratings, POS and WordNet-based features. The best performing system of ours achieves an overall F1 score of 0.570 on All POS category and 0.605 on the Verbs category at the Metaphor Shared Task 2018.
%R 10.18653/v1/W18-0914
%U https://aclanthology.org/W18-0914
%U https://doi.org/10.18653/v1/W18-0914
%P 115-120

Markdown (Informal)

[Di-LSTM Contrast : A Deep Neural Network for Metaphor Detection](https://aclanthology.org/W18-0914) (Swarnkar & Singh, Fig-Lang 2018)

• Di-LSTM Contrast : A Deep Neural Network for Metaphor Detection (Swarnkar & Singh, Fig-Lang 2018)

ACL

• Krishnkant Swarnkar and Anil Kumar Singh. 2018. Di-LSTM Contrast : A Deep Neural Network for Metaphor Detection. In Proceedings of the Workshop on Figurative Language Processing, pages 115–120, New Orleans, Louisiana. Association for Computational Linguistics.