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International Conference on Green, Pervasive, and Cloud Computing

GPC 2018: Green, Pervasive, and Cloud Computing pp 465–473Cite as

Named Entity Recognition Based on BiRHN and CRF

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11204))

Abstract

Named entity recognition is one of the basic work in the field of natural language processing. By utilizing bidirectional LSTM, Lample achieved the best results in the field of named entity recognition in 2016. In this paper, we propose a new neural network structure based on Recurrent Highway Networks (BiRHN for short) and Conditional Random Field (CRF for short). RHN is a good solution to the problem caused by gradients, which extends the LSTM architecture to allow step-to-step transition depths larger than one. Experiments on several datasets show that our model achieves better results (F1 values) than Lample.

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Authors and Affiliations

  1. National University of Defense Technology, Changsha, 410000, Hunan, China

    DongYang Zhao

Authors
  1. DongYang Zhao
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Correspondence to DongYang Zhao .

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Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Shijian Li

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Cite this paper

Zhao, D. (2019). Named Entity Recognition Based on BiRHN and CRF. In: Li, S. (eds) Green, Pervasive, and Cloud Computing. GPC 2018. Lecture Notes in Computer Science(), vol 11204. Springer, Cham. https://doi.org/10.1007/978-3-030-15093-8_33

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  • DOI: https://doi.org/10.1007/978-3-030-15093-8_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-15092-1

  • Online ISBN: 978-3-030-15093-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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