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        <title>Distant Supervised Relation Extraction with Separate Head-Tail CNN</title>
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            <namePart type="given">Alan</namePart>
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    <abstract>Distant supervised relation extraction is an efficient and effective strategy to find relations between entities in texts. However, it inevitably suffers from mislabeling problem and the noisy data will hinder the performance. In this paper, we propose the Separate Head-Tail Convolution Neural Network (SHTCNN), a novel neural relation extraction framework to alleviate this issue. In this method, we apply separate convolution and pooling to the head and tail entity respectively for extracting better semantic features of sentences, and coarse-to-fine strategy to filter out instances which do not have actual relations in order to alleviate noisy data issues. Experiments on a widely used dataset show that our model achieves significant and consistent improvements in relation extraction compared to statistical and vanilla CNN-based methods.</abstract>
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    <identifier type="doi">10.18653/v1/D19-5533</identifier>
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%0 Conference Proceedings
%T Distant Supervised Relation Extraction with Separate Head-Tail CNN
%A Xing, Rui
%A Luo, Jie
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F xing-luo-2019-distant
%X Distant supervised relation extraction is an efficient and effective strategy to find relations between entities in texts. However, it inevitably suffers from mislabeling problem and the noisy data will hinder the performance. In this paper, we propose the Separate Head-Tail Convolution Neural Network (SHTCNN), a novel neural relation extraction framework to alleviate this issue. In this method, we apply separate convolution and pooling to the head and tail entity respectively for extracting better semantic features of sentences, and coarse-to-fine strategy to filter out instances which do not have actual relations in order to alleviate noisy data issues. Experiments on a widely used dataset show that our model achieves significant and consistent improvements in relation extraction compared to statistical and vanilla CNN-based methods.
%R 10.18653/v1/D19-5533
%U https://aclanthology.org/D19-5533
%U https://doi.org/10.18653/v1/D19-5533
%P 249-258

Markdown (Informal)

[Distant Supervised Relation Extraction with Separate Head-Tail CNN](https://aclanthology.org/D19-5533) (Xing & Luo, WNUT 2019)

• Distant Supervised Relation Extraction with Separate Head-Tail CNN (Xing & Luo, WNUT 2019)

ACL

• Rui Xing and Jie Luo. 2019. Distant Supervised Relation Extraction with Separate Head-Tail CNN. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 249–258, Hong Kong, China. Association for Computational Linguistics.