<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="vashishth-etal-2019-graph">
    <titleInfo>
        <title>Graph-based Deep Learning in Natural Language Processing</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Shikhar</namePart>
        <namePart type="family">Vashishth</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Naganand</namePart>
        <namePart type="family">Yadati</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Partha</namePart>
        <namePart type="family">Talukdar</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-nov</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): Tutorial Abstracts</title>
        </titleInfo>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Hong Kong, China</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>This tutorial aims to introduce recent advances in graph-based deep learning techniques such as Graph Convolutional Networks (GCNs) for Natural Language Processing (NLP). It provides a brief introduction to deep learning methods on non-Euclidean domains such as graphs and justifies their relevance in NLP. It then covers recent advances in applying graph-based deep learning methods for various NLP tasks, such as semantic role labeling, machine translation, relationship extraction, and many more.</abstract>
    <identifier type="citekey">vashishth-etal-2019-graph</identifier>
    <location>
        <url>https://aclanthology.org/D19-2006</url>
    </location>
    <part>
        <date>2019-nov</date>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Graph-based Deep Learning in Natural Language Processing
%A Vashishth, Shikhar
%A Yadati, Naganand
%A Talukdar, Partha
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): Tutorial Abstracts
%D 2019
%8 nov
%I Association for Computational Linguistics
%C Hong Kong, China
%F vashishth-etal-2019-graph
%X This tutorial aims to introduce recent advances in graph-based deep learning techniques such as Graph Convolutional Networks (GCNs) for Natural Language Processing (NLP). It provides a brief introduction to deep learning methods on non-Euclidean domains such as graphs and justifies their relevance in NLP. It then covers recent advances in applying graph-based deep learning methods for various NLP tasks, such as semantic role labeling, machine translation, relationship extraction, and many more.
%U https://aclanthology.org/D19-2006

Markdown (Informal)

[Graph-based Deep Learning in Natural Language Processing](https://aclanthology.org/D19-2006) (Vashishth et al., EMNLP 2019)

• Graph-based Deep Learning in Natural Language Processing (Vashishth et al., EMNLP 2019)

ACL

• Shikhar Vashishth, Naganand Yadati, and Partha Talukdar. 2019. Graph-based Deep Learning in Natural Language Processing. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): Tutorial Abstracts, Hong Kong, China. Association for Computational Linguistics.