<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="he-etal-2017-neural">
    <titleInfo>
        <title>Neural Networks for Negation Cue Detection in Chinese</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Hangfeng</namePart>
        <namePart type="family">He</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Federico</namePart>
        <namePart type="family">Fancellu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Bonnie</namePart>
        <namePart type="family">Webber</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-04</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Workshop Computational Semantics Beyond Events and Roles</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Eduardo</namePart>
            <namePart type="family">Blanco</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Roser</namePart>
            <namePart type="family">Morante</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Roser</namePart>
            <namePart type="family">Saurí</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Valencia, Spain</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Negation cue detection involves identifying the span inherently expressing negation in a negative sentence. In Chinese, negative cue detection is complicated by morphological proprieties of the language. Previous work has shown that negative cue detection in Chinese can benefit from specific lexical and morphemic features, as well as cross-lingual information. We show here that they are not necessary: A bi-directional LSTM can perform equally well, with minimal feature engineering. In particular, the use of a character-based model allows us to capture characteristics of negation cues in Chinese using word-embedding information only. Not only does our model performs on par with previous work, further error analysis clarifies what problems remain to be addressed.</abstract>
    <identifier type="citekey">he-etal-2017-neural</identifier>
    <identifier type="doi">10.18653/v1/W17-1809</identifier>
    <location>
        <url>https://aclanthology.org/W17-1809</url>
    </location>
    <part>
        <date>2017-04</date>
        <extent unit="page">
            <start>59</start>
            <end>63</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Neural Networks for Negation Cue Detection in Chinese
%A He, Hangfeng
%A Fancellu, Federico
%A Webber, Bonnie
%Y Blanco, Eduardo
%Y Morante, Roser
%Y Saurí, Roser
%S Proceedings of the Workshop Computational Semantics Beyond Events and Roles
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F he-etal-2017-neural
%X Negation cue detection involves identifying the span inherently expressing negation in a negative sentence. In Chinese, negative cue detection is complicated by morphological proprieties of the language. Previous work has shown that negative cue detection in Chinese can benefit from specific lexical and morphemic features, as well as cross-lingual information. We show here that they are not necessary: A bi-directional LSTM can perform equally well, with minimal feature engineering. In particular, the use of a character-based model allows us to capture characteristics of negation cues in Chinese using word-embedding information only. Not only does our model performs on par with previous work, further error analysis clarifies what problems remain to be addressed.
%R 10.18653/v1/W17-1809
%U https://aclanthology.org/W17-1809
%U https://doi.org/10.18653/v1/W17-1809
%P 59-63

Markdown (Informal)

[Neural Networks for Negation Cue Detection in Chinese](https://aclanthology.org/W17-1809) (He et al., SemBEaR 2017)

• Neural Networks for Negation Cue Detection in Chinese (He et al., SemBEaR 2017)

ACL

• Hangfeng He, Federico Fancellu, and Bonnie Webber. 2017. Neural Networks for Negation Cue Detection in Chinese. In Proceedings of the Workshop Computational Semantics Beyond Events and Roles, pages 59–63, Valencia, Spain. Association for Computational Linguistics.