<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="tayal-etal-2020-regularized">
    <titleInfo>
        <title>Regularized Graph Convolutional Networks for Short Text Classification</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Kshitij</namePart>
        <namePart type="family">Tayal</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Nikhil</namePart>
        <namePart type="family">Rao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Saurabh</namePart>
        <namePart type="family">Agarwal</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Xiaowei</namePart>
        <namePart type="family">Jia</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Karthik</namePart>
        <namePart type="family">Subbian</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Vipin</namePart>
        <namePart type="family">Kumar</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 28th International Conference on Computational Linguistics: Industry Track</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Ann</namePart>
            <namePart type="family">Clifton</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Courtney</namePart>
            <namePart type="family">Napoles</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>International Committee on Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Short text classification is a fundamental problem in natural language processing, social network analysis, and e-commerce. The lack of structure in short text sequences limits the success of popular NLP methods based on deep learning. Simpler methods that rely on bag-of-words representations tend to perform on par with complex deep learning methods. To tackle the limitations of textual features in short text, we propose a Graph-regularized Graph Convolution Network (GR-GCN), which augments graph convolution networks by incorporating label dependencies in the output space. Our model achieves state-of-the-art results on both proprietary and external datasets, outperforming several baseline methods by up to 6% . Furthermore, we show that compared to baseline methods, GR-GCN is more robust to noise in textual features.</abstract>
    <identifier type="citekey">tayal-etal-2020-regularized</identifier>
    <identifier type="doi">10.18653/v1/2020.coling-industry.22</identifier>
    <location>
        <url>https://aclanthology.org/2020.coling-industry.22/</url>
    </location>
    <part>
        <date>2020-12</date>
        <extent unit="page">
            <start>236</start>
            <end>242</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Regularized Graph Convolutional Networks for Short Text Classification
%A Tayal, Kshitij
%A Rao, Nikhil
%A Agarwal, Saurabh
%A Jia, Xiaowei
%A Subbian, Karthik
%A Kumar, Vipin
%Y Clifton, Ann
%Y Napoles, Courtney
%S Proceedings of the 28th International Conference on Computational Linguistics: Industry Track
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Online
%F tayal-etal-2020-regularized
%X Short text classification is a fundamental problem in natural language processing, social network analysis, and e-commerce. The lack of structure in short text sequences limits the success of popular NLP methods based on deep learning. Simpler methods that rely on bag-of-words representations tend to perform on par with complex deep learning methods. To tackle the limitations of textual features in short text, we propose a Graph-regularized Graph Convolution Network (GR-GCN), which augments graph convolution networks by incorporating label dependencies in the output space. Our model achieves state-of-the-art results on both proprietary and external datasets, outperforming several baseline methods by up to 6% . Furthermore, we show that compared to baseline methods, GR-GCN is more robust to noise in textual features.
%R 10.18653/v1/2020.coling-industry.22
%U https://aclanthology.org/2020.coling-industry.22/
%U https://doi.org/10.18653/v1/2020.coling-industry.22
%P 236-242

Markdown (Informal)

[Regularized Graph Convolutional Networks for Short Text Classification](https://aclanthology.org/2020.coling-industry.22/) (Tayal et al., COLING 2020)

• Regularized Graph Convolutional Networks for Short Text Classification (Tayal et al., COLING 2020)

ACL

• Kshitij Tayal, Nikhil Rao, Saurabh Agarwal, Xiaowei Jia, Karthik Subbian, and Vipin Kumar. 2020. Regularized Graph Convolutional Networks for Short Text Classification. In Proceedings of the 28th International Conference on Computational Linguistics: Industry Track, pages 236–242, Online. International Committee on Computational Linguistics.