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Relation Classification Using Coarse and Fine-Grained Networks with SDP Supervised Key Words Selection

  • Yiping Sun
  • Yu Cui
  • Jinglu Hu
  • Weijia Jia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11061)

Abstract

In relation classification, previous work focused on either whole sentence or key words, meeting problems when sentence contains noise or key words are extracted falsely. In this paper, we propose coarse and fine-grained networks for relation classification, which combine sentence and key words together to be more robust. Then, we propose a word selection network under shortest dependency path (SDP) supervision to select key words automatically instead of pre-processed key words and attention, which guides word selection network to a better feature space. A novel opposite loss is also proposed by pushing useful information in unselected words back to selected ones. In SemEval-2010 Task 8, results show that under the same features, proposed method outperforms state-of-the-art methods for relation classification.

Keywords

Relation classification Coarse and fine-grained networks Key words selection Shortest dependency path Opposite loss 

Notes

Acknowledgements

This work is supported by FDCT 0007/2018/A1, DCT-MoST Joint-project No. (025/2015/AMJ) of SAR Macau; University of Macau Funds Nos: CPG2018-00032-FST & SRG2018-00111-FST; Chinese National Research Fund (NSFC) Key Project No. 61532013; National China 973 Project No. 2015CB352401 and 985 Project of Shanghai Jiao Tong University: WF220103001. We also thank Xinsong ZHANG, Lester James V. Miranda and Mingyang YU for revising this paper.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Electronic Information and Electrical EngineeringShanghai JiaoTong UniversityShanghaiChina
  2. 2.Graduate School of Information, Production and SystemsWaseda UniversityKitakyushu-shiJapan
  3. 3.University of MacauMacauChina

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