<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="pramanick-bhattacharya-2021-joint">
    <titleInfo>
        <title>Joint Learning of Representations for Web-tables, Entities and Types using Graph Convolutional Network</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Aniket</namePart>
        <namePart type="family">Pramanick</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Indrajit</namePart>
        <namePart type="family">Bhattacharya</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-04</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Paola</namePart>
            <namePart type="family">Merlo</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jorg</namePart>
            <namePart type="family">Tiedemann</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Reut</namePart>
            <namePart type="family">Tsarfaty</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Existing approaches for table annotation with entities and types either capture the structure of table using graphical models, or learn embeddings of table entries without accounting for the complete syntactic structure. We propose TabGCN, that uses Graph Convolutional Networks to capture the complete structure of tables, knowledge graph and the training annotations, and jointly learns embeddings for table elements as well as the entities and types. To account for knowledge incompleteness, TabGCN’s embeddings can be used to discover new entities and types. Using experiments on 5 benchmark datasets, we show that TabGCN significantly outperforms multiple state-of-the-art baselines for table annotation, while showing promising performance on downstream table-related applications.</abstract>
    <identifier type="citekey">pramanick-bhattacharya-2021-joint</identifier>
    <identifier type="doi">10.18653/v1/2021.eacl-main.102</identifier>
    <location>
        <url>https://aclanthology.org/2021.eacl-main.102</url>
    </location>
    <part>
        <date>2021-04</date>
        <extent unit="page">
            <start>1197</start>
            <end>1206</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Joint Learning of Representations for Web-tables, Entities and Types using Graph Convolutional Network
%A Pramanick, Aniket
%A Bhattacharya, Indrajit
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F pramanick-bhattacharya-2021-joint
%X Existing approaches for table annotation with entities and types either capture the structure of table using graphical models, or learn embeddings of table entries without accounting for the complete syntactic structure. We propose TabGCN, that uses Graph Convolutional Networks to capture the complete structure of tables, knowledge graph and the training annotations, and jointly learns embeddings for table elements as well as the entities and types. To account for knowledge incompleteness, TabGCN’s embeddings can be used to discover new entities and types. Using experiments on 5 benchmark datasets, we show that TabGCN significantly outperforms multiple state-of-the-art baselines for table annotation, while showing promising performance on downstream table-related applications.
%R 10.18653/v1/2021.eacl-main.102
%U https://aclanthology.org/2021.eacl-main.102
%U https://doi.org/10.18653/v1/2021.eacl-main.102
%P 1197-1206

Markdown (Informal)

[Joint Learning of Representations for Web-tables, Entities and Types using Graph Convolutional Network](https://aclanthology.org/2021.eacl-main.102) (Pramanick & Bhattacharya, EACL 2021)

• Joint Learning of Representations for Web-tables, Entities and Types using Graph Convolutional Network (Pramanick & Bhattacharya, EACL 2021)

ACL

• Aniket Pramanick and Indrajit Bhattacharya. 2021. Joint Learning of Representations for Web-tables, Entities and Types using Graph Convolutional Network. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1197–1206, Online. Association for Computational Linguistics.