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Efficient relation extraction method based on spatial feature using ELM

  • Extreme Learning Machine and Applications
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Entity relation extraction can be applied in the automatic question answering system, digital library and many other fields. However, the previous works on this topic mainly focused on the features from a sentence itself in the data sets, without considering the links between sentences in the corpus. In this paper, we propose a concept model and obtain a new effective spatial feature based on this concept model. The added feature makes our feature space concerning not only the inherent information of the sentence itself, but also the semantic information connection between sentences. At last, we use ELM as the training classifier in entity relation extraction. The experiment result shows that the precision and recall of the relation extraction both have a significant increase, by using the new feature. Also, the use of ELM significantly reduces the time of relation extraction. It has a better performance than the traditional method based on SVM.

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Notes

  1. Data set: http://www.wikipedia.org/.

  2. ELM Source Codes: http://www.ntu.edu.sg/home/egbhuang/.

  3. Data set: http://www.csie.ntu.edu.tw/cjlin/libsvm/.

References

  1. ACE (1999–2008) Automatic content extraction. http://www.ldc.upenn.edu/Projects/ACE

  2. Jiang J, Zhai CX (2007) A systematic exploration of the feature space for relation extraction. In: Proceedings of NAACL HLT, pp 113–120

  3. Kambhatla N (2004) Combining lexical, syntactic and semantic features with maximum entropy models for extracting relations. In: Proceedings of the annual meeting of the association for computational linguistics, Barcelona, Spain, pp 21–26

  4. Zhao SB, Grishman R (2005) Extracting relations with integrated information using kernel-based methods. IN: Proceedings of the annual meeting of the association for computational linguistics, University of Michigan-Ann Arbor, USA, pp 419–426

  5. Zhou GD, Su J, Zhang J, Zhang M (2005) Exploring various knowledge in relation extraction. In: Proceedings of the annual meeting of the association for computational linguistics, Ann Arbor, Michgan, USA, pp 427–434

  6. Zhou GD, Su J, Zhang M (2006) Modeling commonality among related classes in relation extraction. In: Proceedings of the international conference on computational linguistics and the annual meeting of the association for computational linguistics, Sydney, Australia, pp 121–128

  7. Zhou GD, Zhang M (2007) Extracting relation information from text documents by exploring various types of knowledge. Inf Process Manag 43:969–982

    Article  Google Scholar 

  8. Zhou GD, Zhang M, Ji DH, Zhu QM (2008) Hierarchical learning strategy in semantic relation extraction. Inf Process Manag 44:1008–1021

    Article  Google Scholar 

  9. Zhang M, Che WX, Zhou GD, Aw AT, Tan CL, Liu T, Li S (2008) Semantic role labeling using a grammar-driven convolution tree kernel. IEEE Trans Audio Speech Lang Process 16(7):1315–1329

    Article  Google Scholar 

  10. Zelenko D, Aone C, Richardella A (2003) Kernel methods for relation extraction. J Mach Learn Res 3:1083–1106

    MATH  MathSciNet  Google Scholar 

  11. Culotta A, Sorensen J (2004) Dependency tree kernels for relation extraction. In: Proceedings of the 42nd annual meeting on association for computational linguistics, Barcelona, Spain, p 423-es (July 21–26, 2004)

  12. Huang GB, Zhou H, Ding X, Zhang R (2012) Extreme learning machinefor regression and multiclass classifcation. IEEE Trans Syst Man Cybern B Cybern 42(2):513–529

    Article  Google Scholar 

  13. Bottou L, Cortes C, Denker J, Drucker H, Guyon I, Jackel L, LeCun Y, Muller U, Sackinger E, Simard P, Vapnik V (1994) Comparison of classifier methods: a case study in handwriting digit recognition. In: Proceedings of international conference pattern recognition, IEEE Computer Society Press, pp 77–87

  14. Platt JC, Cristianini N, Shawe-Taylor J (2000) Large margin DAG’s for multiclass classification. Adv Neural Inf Process Syst 12:547–553 MIT Press

    Google Scholar 

  15. Zhao X-G, Wang G, Bi X, Gong P, Zhao Y (2011) XML document classification based on ELM. Neurocomputing 74(16):2444–2451

    Article  Google Scholar 

  16. Binu Chacko P, Vimal Krishnan VR, Raju G, Babu Anto P (2012) Handwritten character recognition using wavelet energy and extreme learning machine. Int J Mach Learn Cyber 3(2):149–161

    Article  Google Scholar 

  17. Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501

    Article  Google Scholar 

  18. Kambhatla N (2004) Combining lexical, syntactic and semantic features with maximum entropy models for extracting relations. In: Proceedings of the ACL 2004 on interactive poster and demonstration sessions, Barcelona, Spain, pp 178–181

  19. Zhou G, Su J, Zhang J, Zhang M (2005) Exploring various knowledge in relation extraction. In: Proceedings of the 43rd annual meeting on association for computational linguistics, Ann Arbor, Michigan, pp 427–434

  20. Jiang J, Zhai CX (2007) A systematic exploration of the feature space for relation extraction. In: Proceedings of the annual conference of the North American chapter of the association for computational linguistics, Rochester, NY, pp 113–120

  21. Qin Y, Zheng D, Zhao T (2012) Research on search results optimization technology with category features integration. Int J Mach Learn Cybern 3(1):71–76

    Article  Google Scholar 

  22. Haussler D (1999) Convolution kernels on discrete structures. In: University of California, Santa Cruz, Technical Report, pp 1–38

  23. Zelenko D, Aone C, Richardella A (2003) Kernel methods for relation extraction. J Mach Learn Res 3:1083–1106

    MATH  MathSciNet  Google Scholar 

  24. Culotta A, Sorensen J (2004) Dependency tree kernels for relation extraction. In: Proceedings of ACL, Spain, pp 423–429

  25. Bunescu R, Mooney RJ (2005) A shortest path dependency kernel for relation extraction. In: Proceedings of the conference on human language technology and empirical methods in natural language processing. Association for computational linguistics, Vancouver, pp 724–731

  26. Zhao S, Grishman R (2005) Extracting relations with integrated information using kernel methods. In: ACL-2005, Ann Arbor, USA, pp 419–426

  27. Zhou GD, Zhang M, Ji DH, Zhu QM (2007) Tree kernel-based relation extraction with context-sensitive structured parse tree information. In: Proceedings of EMNLP-CoNLL, pp 728–736

  28. Zhou GD, Qian LH, Fan JX (2010) Tree kernel-based semantic relation extraction with rich syntactic and semantic information. Inf Sci 180(8):1313–1325

    Article  MathSciNet  Google Scholar 

  29. Yang X, Mao K (2014) Reduced ELMs for causal relation extraction from unconstructed Text. In: IEEE intelligent systems, pp 20–23 (Nov/Dec 2014)

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Acknowledgments

This research was partially supported by the National Natural Science Foundation of China under Grant Nos. 61332006 and 61100022; the National Basic Research Program of China under Grant No. 2011CB302200-G; the 863 Program under Grant No. 2012AA011004.

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Authors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, Liaoning, 110819, Shenyang, China

    Huilin Liu, Chunfeng Jiang & Chunyan Hu

  2. School of Information, Renmin University of China, Beijing, 100872, China

    Liyuan Zhang

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  1. Huilin Liu
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  2. Chunfeng Jiang
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  3. Chunyan Hu
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  4. Liyuan Zhang
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Correspondence to Huilin Liu.

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Liu, H., Jiang, C., Hu, C. et al. Efficient relation extraction method based on spatial feature using ELM. Neural Comput & Applic 27, 271–281 (2016). https://doi.org/10.1007/s00521-014-1776-9

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