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Efficient relation extraction method based on spatial feature using ELM

  • Extreme Learning Machine and Applications
  • Published:
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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/.

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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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