<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="chen-qian-2019-transfer">
    <titleInfo>
        <title>Transfer Capsule Network for Aspect Level Sentiment Classification</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Zhuang</namePart>
        <namePart type="family">Chen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Tieyun</namePart>
        <namePart type="family">Qian</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Traum</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">Florence, Italy</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Aspect-level sentiment classification aims to determine the sentiment polarity of a sentence towards an aspect. Due to the high cost in annotation, the lack of aspect-level labeled data becomes a major obstacle in this area. On the other hand, document-level labeled data like reviews are easily accessible from online websites. These reviews encode sentiment knowledge in abundant contexts. In this paper, we propose a Transfer Capsule Network (TransCap) model for transferring document-level knowledge to aspect-level sentiment classification. To this end, we first develop an aspect routing approach to encapsulate the sentence-level semantic representations into semantic capsules from both the aspect-level and document-level data. We then extend the dynamic routing approach to adaptively couple the semantic capsules with the class capsules under the transfer learning framework. Experiments on SemEval datasets demonstrate the effectiveness of TransCap.</abstract>
    <identifier type="citekey">chen-qian-2019-transfer</identifier>
    <identifier type="doi">10.18653/v1/P19-1052</identifier>
    <location>
        <url>https://aclanthology.org/P19-1052</url>
    </location>
    <part>
        <date>2019-07</date>
        <extent unit="page">
            <start>547</start>
            <end>556</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Transfer Capsule Network for Aspect Level Sentiment Classification
%A Chen, Zhuang
%A Qian, Tieyun
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F chen-qian-2019-transfer
%X Aspect-level sentiment classification aims to determine the sentiment polarity of a sentence towards an aspect. Due to the high cost in annotation, the lack of aspect-level labeled data becomes a major obstacle in this area. On the other hand, document-level labeled data like reviews are easily accessible from online websites. These reviews encode sentiment knowledge in abundant contexts. In this paper, we propose a Transfer Capsule Network (TransCap) model for transferring document-level knowledge to aspect-level sentiment classification. To this end, we first develop an aspect routing approach to encapsulate the sentence-level semantic representations into semantic capsules from both the aspect-level and document-level data. We then extend the dynamic routing approach to adaptively couple the semantic capsules with the class capsules under the transfer learning framework. Experiments on SemEval datasets demonstrate the effectiveness of TransCap.
%R 10.18653/v1/P19-1052
%U https://aclanthology.org/P19-1052
%U https://doi.org/10.18653/v1/P19-1052
%P 547-556

Markdown (Informal)

[Transfer Capsule Network for Aspect Level Sentiment Classification](https://aclanthology.org/P19-1052) (Chen & Qian, ACL 2019)

• Transfer Capsule Network for Aspect Level Sentiment Classification (Chen & Qian, ACL 2019)

ACL

• Zhuang Chen and Tieyun Qian. 2019. Transfer Capsule Network for Aspect Level Sentiment Classification. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 547–556, Florence, Italy. Association for Computational Linguistics.