<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="rahimi-etal-2018-semi">
    <titleInfo>
        <title>Semi-supervised User Geolocation via Graph Convolutional Networks</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Afshin</namePart>
        <namePart type="family">Rahimi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Trevor</namePart>
        <namePart type="family">Cohn</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Timothy</namePart>
        <namePart type="family">Baldwin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Melbourne, Australia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Social media user geolocation is vital to many applications such as event detection. In this paper, we propose GCN, a multiview geolocation model based on Graph Convolutional Networks, that uses both text and network context. We compare GCN to the state-of-the-art, and to two baselines we propose, and show that our model achieves or is competitive with the state-of-the-art over three benchmark geolocation datasets when sufficient supervision is available. We also evaluate GCN under a minimal supervision scenario, and show it outperforms baselines. We find that highway network gates are essential for controlling the amount of useful neighbourhood expansion in GCN.</abstract>
    <identifier type="citekey">rahimi-etal-2018-semi</identifier>
    <identifier type="doi">10.18653/v1/P18-1187</identifier>
    <location>
        <url>https://aclanthology.org/P18-1187</url>
    </location>
    <part>
        <date>2018-07</date>
        <extent unit="page">
            <start>2009</start>
            <end>2019</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Semi-supervised User Geolocation via Graph Convolutional Networks
%A Rahimi, Afshin
%A Cohn, Trevor
%A Baldwin, Timothy
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F rahimi-etal-2018-semi
%X Social media user geolocation is vital to many applications such as event detection. In this paper, we propose GCN, a multiview geolocation model based on Graph Convolutional Networks, that uses both text and network context. We compare GCN to the state-of-the-art, and to two baselines we propose, and show that our model achieves or is competitive with the state-of-the-art over three benchmark geolocation datasets when sufficient supervision is available. We also evaluate GCN under a minimal supervision scenario, and show it outperforms baselines. We find that highway network gates are essential for controlling the amount of useful neighbourhood expansion in GCN.
%R 10.18653/v1/P18-1187
%U https://aclanthology.org/P18-1187
%U https://doi.org/10.18653/v1/P18-1187
%P 2009-2019

Markdown (Informal)

[Semi-supervised User Geolocation via Graph Convolutional Networks](https://aclanthology.org/P18-1187) (Rahimi et al., ACL 2018)

• Semi-supervised User Geolocation via Graph Convolutional Networks (Rahimi et al., ACL 2018)

ACL

• Afshin Rahimi, Trevor Cohn, and Timothy Baldwin. 2018. Semi-supervised User Geolocation via Graph Convolutional Networks. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2009–2019, Melbourne, Australia. Association for Computational Linguistics.