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        <title>Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network</title>
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        <namePart type="given">Sunil</namePart>
        <namePart type="given">Kumar</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Sophia</namePart>
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            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</title>
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    <abstract>Inter-sentence relation extraction deals with a number of complex semantic relationships in documents, which require local, non-local, syntactic and semantic dependencies. Existing methods do not fully exploit such dependencies. We present a novel inter-sentence relation extraction model that builds a labelled edge graph convolutional neural network model on a document-level graph. The graph is constructed using various inter- and intra-sentence dependencies to capture local and non-local dependency information. In order to predict the relation of an entity pair, we utilise multi-instance learning with bi-affine pairwise scoring. Experimental results show that our model achieves comparable performance to the state-of-the-art neural models on two biochemistry datasets. Our analysis shows that all the types in the graph are effective for inter-sentence relation extraction.</abstract>
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%0 Conference Proceedings
%T Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network
%A Sahu, Sunil Kumar
%A Christopoulou, Fenia
%A Miwa, Makoto
%A Ananiadou, Sophia
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F sahu-etal-2019-inter
%X Inter-sentence relation extraction deals with a number of complex semantic relationships in documents, which require local, non-local, syntactic and semantic dependencies. Existing methods do not fully exploit such dependencies. We present a novel inter-sentence relation extraction model that builds a labelled edge graph convolutional neural network model on a document-level graph. The graph is constructed using various inter- and intra-sentence dependencies to capture local and non-local dependency information. In order to predict the relation of an entity pair, we utilise multi-instance learning with bi-affine pairwise scoring. Experimental results show that our model achieves comparable performance to the state-of-the-art neural models on two biochemistry datasets. Our analysis shows that all the types in the graph are effective for inter-sentence relation extraction.
%R 10.18653/v1/P19-1423
%U https://aclanthology.org/P19-1423/
%U https://doi.org/10.18653/v1/P19-1423
%P 4309-4316

Markdown (Informal)

[Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network](https://aclanthology.org/P19-1423/) (Sahu et al., ACL 2019)

• Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network (Sahu et al., ACL 2019)

ACL

• Sunil Kumar Sahu, Fenia Christopoulou, Makoto Miwa, and Sophia Ananiadou. 2019. Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4309–4316, Florence, Italy. Association for Computational Linguistics.