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Joint Extraction of Multiple Relations and Entities by Using a Hybrid Neural Network

  • Peng Zhou
  • Suncong Zheng
  • Jiaming Xu
  • Zhenyu QiEmail author
  • Hongyun Bao
  • Bo Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10565)

Abstract

This paper proposes a novel end-to-end neural model to jointly extract entities and relations in a sentence. Unlike most existing approaches, the proposed model uses a hybrid neural network to automatically learn sentence features and does not rely on any Natural Language Processing (NLP) tools, such as dependency parser. Our model is further capable of modeling multiple relations and their corresponding entity pairs simultaneously. Experiments on the CoNLL04 dataset demonstrate that our model using only word embeddings as input features achieves state-of-the-art performance.

Keywords

Information extraction Neural networks 

Notes

Acknowledgments

This research was supported by the National High Technology Research and Development Program of China (No. 2015AA015402) and the National Natural Science Foundation of China (No. 61602479). We thank the anonymous reviewers for their insightful comments.

References

  1. 1.
    Gupta, P., Schutze, H., Andrassy, B.: Table filling multi-task recurrent neural network for joint entity and relation extraction. In: COLING (2016)Google Scholar
  2. 2.
    Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. Comput. Sci. 3(4), 212–223 (2012)Google Scholar
  3. 3.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  4. 4.
    Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging. Comput. Sci. (2015)Google Scholar
  5. 5.
    Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. In: ACL (2014)Google Scholar
  6. 6.
    Kate, R.J., Mooney, R.J.: Joint entity and relation extraction using card-pyramid parsing. In: ACL (2010)Google Scholar
  7. 7.
    Kumar, A., Irsoy, O., Ondruska, P., Iyyer, M., Bradbury, J., Gulrajani, I., Zhong, V., Paulus, R., Socher, R.: Ask me anything: dynamic memory networks for natural language processing. Comput. Sci. (2016)Google Scholar
  8. 8.
    Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. In: NAACL-HLT, pp. 260–270 (2016)Google Scholar
  9. 9.
    Li, Q., Ji, H.: Incremental joint extraction of entity mentions and relations. In: ACL, pp. 402–412 (2014)Google Scholar
  10. 10.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS (2013)Google Scholar
  11. 11.
    Miwa, M., Bansal, M.: End-to-end relation extraction using LSTMs on sequences and tree structures. In: ACL, pp. 1105–1116 (2016)Google Scholar
  12. 12.
    Miwa, M., Sasaki, Y.: Modeling joint entity and relation extraction with table representation. In: EMNLP, pp. 944–948 (2014)Google Scholar
  13. 13.
    Roth, D., Yih, W.: A linear programming formulation for global inference in natural language tasks. Technical report, DTIC Document (2004)Google Scholar
  14. 14.
    Santos, C.N.D., Xiang, B., Zhou, B.: Classifying relations by ranking with convolutional neural networks. Comput. Sci. (2015)Google Scholar
  15. 15.
    Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673–2681 (1997)CrossRefGoogle Scholar
  16. 16.
    Shin, J., Wu, S., Wang, F., De Sa, C., Zhang, C., Ré, C.: Incremental knowledge base construction using deepdive. VLDB Endowm. 8(11), 1310–1321 (2015)CrossRefGoogle Scholar
  17. 17.
    Singh, S., Riedel, S., Martin, B., Zheng, J., Mccallum, A.: Joint inference of entities, relations, and coreference. In: The Workshop on Automated Knowledge Base Construction, pp. 1–6 (2013)Google Scholar
  18. 18.
    Suchanek, F.M., Ifrim, G., Weikum, G.: Combining linguistic and statistical analysis to extract relations from web documents. In: SIGKDD, pp. 712–717 (2006)Google Scholar
  19. 19.
    Vu, N.T., Adel, H., Gupta, P., et al.: Combining recurrent and convolutional neural networks for relation classification. In: NAACL-HLT, pp. 534–539 (2016)Google Scholar
  20. 20.
    Yang, B., Cardie, C.: Joint inference for fine-grained opinion extraction. In: ACL, pp. 1640–1649 (2013)Google Scholar
  21. 21.
    Yao, L., Sun, C., Li, S., Wang, X., Wang, X.: CRF-based active learning for Chinese named entity recognition. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 1557–1561 (2009)Google Scholar
  22. 22.
    Zeiler, M.D.: ADADELTA: an adaptive learning rate method. Comput. Sci. (2012)Google Scholar
  23. 23.
    Zelenko, D., Aone, C., Richardella, A.: Kernel methods for relation extraction. J. Mach. Learn. Res. 3(3), 1083–1106 (2010)MathSciNetzbMATHGoogle Scholar
  24. 24.
    Zeng, D., Liu, K., Lai, S., Zhou, G., Zhao, J., et al.: Relation classification via convolutional deep neural network. In: COLING, pp. 2335–2344 (2014)Google Scholar
  25. 25.
    Zhang, D., Wang, D.: Relation classification via recurrent neural network. Comput. Sci. (2015)Google Scholar
  26. 26.
    Zheng, S., Hao, Y., Lu, D., Bao, H., Xu, J., Hao, H., Xu, B.: Joint entity and relation extraction based on a hybrid neural network. Neurocomputing (2017)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Peng Zhou
    • 1
    • 2
  • Suncong Zheng
    • 1
    • 2
  • Jiaming Xu
    • 1
  • Zhenyu Qi
    • 1
    Email author
  • Hongyun Bao
    • 1
  • Bo Xu
    • 1
    • 2
  1. 1.Institute of Automation, Chinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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