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        <title>A Hierarchical Entity Graph Convolutional Network for Relation Extraction across Documents</title>
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    <name type="personal">
        <namePart type="given">Tapas</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
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    <abstract>Distantly supervised datasets for relation extraction mostly focus on sentence-level extraction, and they cover very few relations. In this work, we propose cross-document relation extraction, where the two entities of a relation tuple appear in two different documents that are connected via a chain of common entities. Following this idea, we create a dataset for two-hop relation extraction, where each chain contains exactly two documents. Our proposed dataset covers a higher number of relations than the publicly available sentence-level datasets. We also propose a hierarchical entity graph convolutional network (HEGCN) model for this task that improves performance by 1.1% F1 score on our two-hop relation extraction dataset, compared to some strong neural baselines.</abstract>
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        <date>2021-09</date>
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%0 Conference Proceedings
%T A Hierarchical Entity Graph Convolutional Network for Relation Extraction across Documents
%A Nayak, Tapas
%A Ng, Hwee Tou
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F nayak-ng-2021-hierarchical
%X Distantly supervised datasets for relation extraction mostly focus on sentence-level extraction, and they cover very few relations. In this work, we propose cross-document relation extraction, where the two entities of a relation tuple appear in two different documents that are connected via a chain of common entities. Following this idea, we create a dataset for two-hop relation extraction, where each chain contains exactly two documents. Our proposed dataset covers a higher number of relations than the publicly available sentence-level datasets. We also propose a hierarchical entity graph convolutional network (HEGCN) model for this task that improves performance by 1.1% F1 score on our two-hop relation extraction dataset, compared to some strong neural baselines.
%U https://aclanthology.org/2021.ranlp-1.115
%P 1022-1030

Markdown (Informal)

[A Hierarchical Entity Graph Convolutional Network for Relation Extraction across Documents](https://aclanthology.org/2021.ranlp-1.115) (Nayak & Ng, RANLP 2021)

• A Hierarchical Entity Graph Convolutional Network for Relation Extraction across Documents (Nayak & Ng, RANLP 2021)

ACL

• Tapas Nayak and Hwee Tou Ng. 2021. A Hierarchical Entity Graph Convolutional Network for Relation Extraction across Documents. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1022–1030, Held Online. INCOMA Ltd..