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        <title>Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction</title>
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    <abstract>We propose a contextualised graph convolution network over multiple dependency-based sub-graphs for relation extraction. A novel method to construct multiple sub-graphs using words in shortest dependency path and words linked to entities in the dependency parse is proposed. Graph convolution operation is performed over the resulting multiple sub-graphs to obtain more informative features useful for relation extraction. Our experimental results show that the proposed method achieves superior performance over the existing GCN-based models achieving state-of-the-art performance on cross-sentence n-ary relation extraction dataset and SemEval 2010 Task 8 sentence-level relation extraction dataset. Our model also achieves a comparable performance to the SoTA on the TACRED dataset.</abstract>
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%0 Conference Proceedings
%T Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction
%A Mandya, Angrosh
%A Bollegala, Danushka
%A Coenen, Frans
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F mandya-etal-2020-graph
%X We propose a contextualised graph convolution network over multiple dependency-based sub-graphs for relation extraction. A novel method to construct multiple sub-graphs using words in shortest dependency path and words linked to entities in the dependency parse is proposed. Graph convolution operation is performed over the resulting multiple sub-graphs to obtain more informative features useful for relation extraction. Our experimental results show that the proposed method achieves superior performance over the existing GCN-based models achieving state-of-the-art performance on cross-sentence n-ary relation extraction dataset and SemEval 2010 Task 8 sentence-level relation extraction dataset. Our model also achieves a comparable performance to the SoTA on the TACRED dataset.
%R 10.18653/v1/2020.coling-main.565
%U https://aclanthology.org/2020.coling-main.565
%U https://doi.org/10.18653/v1/2020.coling-main.565
%P 6424-6435

Markdown (Informal)

[Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction](https://aclanthology.org/2020.coling-main.565) (Mandya et al., COLING 2020)

• Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction (Mandya et al., COLING 2020)

ACL

• Angrosh Mandya, Danushka Bollegala, and Frans Coenen. 2020. Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6424–6435, Barcelona, Spain (Online). International Committee on Computational Linguistics.