<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="abir-2023-aspect">
    <titleInfo>
        <title>Aspect and Opinion Term Extraction Using Graph Attention Network</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Chakraborty</namePart>
        <namePart type="family">Abir</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 20th International Conference on Natural Language Processing (ICON)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">D</namePart>
            <namePart type="given">Pawar</namePart>
            <namePart type="family">Jyoti</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Lalitha</namePart>
            <namePart type="given">Devi</namePart>
            <namePart type="family">Sobha</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>NLP Association of India (NLPAI)</publisher>
            <place>
                <placeTerm type="text">Goa University, Goa, India</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this work we investigate the capability of Graph Attention Network for extracting aspect and opinion terms. Aspect and opinion term extraction is posed as a token-level classification task akin to named entity recognition. We use the dependency tree of the input query as additional feature in a Graph Attention Network along with the token and part-of-speech features. We show that the dependency structure is a powerful feature that in the presence of a CRF layer substantially improves the performance and generates the best result on the commonly used datasets from SemEval 2014, 2015 and 2016. We experiment with additional layers like BiLSTM and Transformer in addition to the CRF layer. We also show that our approach works well in the presence of multiple aspects or sentiments in the same query and it is not necessary to modify the dependency tree based on a single aspect as was the original application for sentiment classification.</abstract>
    <identifier type="citekey">abir-2023-aspect</identifier>
    <location>
        <url>https://aclanthology.org/2023.icon-1.57</url>
    </location>
    <part>
        <date>2023-12</date>
        <extent unit="page">
            <start>594</start>
            <end>602</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Aspect and Opinion Term Extraction Using Graph Attention Network
%A Abir, Chakraborty
%Y Jyoti, D. Pawar
%Y Sobha, Lalitha Devi
%S Proceedings of the 20th International Conference on Natural Language Processing (ICON)
%D 2023
%8 December
%I NLP Association of India (NLPAI)
%C Goa University, Goa, India
%F abir-2023-aspect
%X In this work we investigate the capability of Graph Attention Network for extracting aspect and opinion terms. Aspect and opinion term extraction is posed as a token-level classification task akin to named entity recognition. We use the dependency tree of the input query as additional feature in a Graph Attention Network along with the token and part-of-speech features. We show that the dependency structure is a powerful feature that in the presence of a CRF layer substantially improves the performance and generates the best result on the commonly used datasets from SemEval 2014, 2015 and 2016. We experiment with additional layers like BiLSTM and Transformer in addition to the CRF layer. We also show that our approach works well in the presence of multiple aspects or sentiments in the same query and it is not necessary to modify the dependency tree based on a single aspect as was the original application for sentiment classification.
%U https://aclanthology.org/2023.icon-1.57
%P 594-602

Markdown (Informal)

[Aspect and Opinion Term Extraction Using Graph Attention Network](https://aclanthology.org/2023.icon-1.57) (Abir, ICON 2023)

• Aspect and Opinion Term Extraction Using Graph Attention Network (Abir, ICON 2023)

ACL

• Chakraborty Abir. 2023. Aspect and Opinion Term Extraction Using Graph Attention Network. In Proceedings of the 20th International Conference on Natural Language Processing (ICON), pages 594–602, Goa University, Goa, India. NLP Association of India (NLPAI).