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        <title>Syntax-aware Multi-task Graph Convolutional Networks for Biomedical Relation Extraction</title>
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        <namePart type="given">Diya</namePart>
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    <name type="personal">
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        <name type="personal">
            <namePart type="given">Antonio</namePart>
            <namePart type="family">Jimeno Yepes</namePart>
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    <abstract>In this paper we tackle two unique challenges in biomedical relation extraction. The first challenge is that the contextual information between two entity mentions often involves sophisticated syntactic structures. We propose a novel graph convolutional networks model that incorporates dependency parsing and contextualized embedding to effectively capture comprehensive contextual information. The second challenge is that most of the benchmark data sets for this task are quite imbalanced because more than 80% mention pairs are negative instances (i.e., no relations). We propose a multi-task learning framework to jointly model relation identification and classification tasks to propagate supervision signals from each other and apply a focal loss to focus training on ambiguous mention pairs. By applying these two strategies, experiments show that our model achieves state-of-the-art F-score on the 2013 drug-drug interaction extraction task.</abstract>
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%0 Conference Proceedings
%T Syntax-aware Multi-task Graph Convolutional Networks for Biomedical Relation Extraction
%A Li, Diya
%A Ji, Heng
%Y Holderness, Eben
%Y Jimeno Yepes, Antonio
%Y Lavelli, Alberto
%Y Minard, Anne-Lyse
%Y Pustejovsky, James
%Y Rinaldi, Fabio
%S Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong
%F li-ji-2019-syntax
%X In this paper we tackle two unique challenges in biomedical relation extraction. The first challenge is that the contextual information between two entity mentions often involves sophisticated syntactic structures. We propose a novel graph convolutional networks model that incorporates dependency parsing and contextualized embedding to effectively capture comprehensive contextual information. The second challenge is that most of the benchmark data sets for this task are quite imbalanced because more than 80% mention pairs are negative instances (i.e., no relations). We propose a multi-task learning framework to jointly model relation identification and classification tasks to propagate supervision signals from each other and apply a focal loss to focus training on ambiguous mention pairs. By applying these two strategies, experiments show that our model achieves state-of-the-art F-score on the 2013 drug-drug interaction extraction task.
%R 10.18653/v1/D19-6204
%U https://aclanthology.org/D19-6204
%U https://doi.org/10.18653/v1/D19-6204
%P 28-33

Markdown (Informal)

[Syntax-aware Multi-task Graph Convolutional Networks for Biomedical Relation Extraction](https://aclanthology.org/D19-6204) (Li & Ji, Louhi 2019)

• Syntax-aware Multi-task Graph Convolutional Networks for Biomedical Relation Extraction (Li & Ji, Louhi 2019)

ACL

• Diya Li and Heng Ji. 2019. Syntax-aware Multi-task Graph Convolutional Networks for Biomedical Relation Extraction. In Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019), pages 28–33, Hong Kong. Association for Computational Linguistics.