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        <title>Graph Convolution over Pruned Dependency Trees Improves Relation Extraction</title>
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    <name type="personal">
        <namePart type="given">Yuhao</namePart>
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    <name type="personal">
        <namePart type="given">Peng</namePart>
        <namePart type="family">Qi</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">David</namePart>
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    <abstract>Dependency trees help relation extraction models capture long-range relations between words. However, existing dependency-based models either neglect crucial information (e.g., negation) by pruning the dependency trees too aggressively, or are computationally inefficient because it is difficult to parallelize over different tree structures. We propose an extension of graph convolutional networks that is tailored for relation extraction, which pools information over arbitrary dependency structures efficiently in parallel. To incorporate relevant information while maximally removing irrelevant content, we further apply a novel pruning strategy to the input trees by keeping words immediately around the shortest path between the two entities among which a relation might hold. The resulting model achieves state-of-the-art performance on the large-scale TACRED dataset, outperforming existing sequence and dependency-based neural models. We also show through detailed analysis that this model has complementary strengths to sequence models, and combining them further improves the state of the art.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1244</identifier>
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        <date>2018-oct-nov</date>
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            <start>2205</start>
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%0 Conference Proceedings
%T Graph Convolution over Pruned Dependency Trees Improves Relation Extraction
%A Zhang, Yuhao
%A Qi, Peng
%A Manning, Christopher D.
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F zhang-etal-2018-graph
%X Dependency trees help relation extraction models capture long-range relations between words. However, existing dependency-based models either neglect crucial information (e.g., negation) by pruning the dependency trees too aggressively, or are computationally inefficient because it is difficult to parallelize over different tree structures. We propose an extension of graph convolutional networks that is tailored for relation extraction, which pools information over arbitrary dependency structures efficiently in parallel. To incorporate relevant information while maximally removing irrelevant content, we further apply a novel pruning strategy to the input trees by keeping words immediately around the shortest path between the two entities among which a relation might hold. The resulting model achieves state-of-the-art performance on the large-scale TACRED dataset, outperforming existing sequence and dependency-based neural models. We also show through detailed analysis that this model has complementary strengths to sequence models, and combining them further improves the state of the art.
%R 10.18653/v1/D18-1244
%U https://aclanthology.org/D18-1244
%U https://doi.org/10.18653/v1/D18-1244
%P 2205-2215

Markdown (Informal)

[Graph Convolution over Pruned Dependency Trees Improves Relation Extraction](https://aclanthology.org/D18-1244) (Zhang et al., EMNLP 2018)

• Graph Convolution over Pruned Dependency Trees Improves Relation Extraction (Zhang et al., EMNLP 2018)

ACL

• Yuhao Zhang, Peng Qi, and Christopher D. Manning. 2018. Graph Convolution over Pruned Dependency Trees Improves Relation Extraction. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2205–2215, Brussels, Belgium. Association for Computational Linguistics.