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Multichannel LSTM-CRF for Named Entity Recognition in Chinese Social Media

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Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data (NLP-NABD 2017, CCL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10565))

Abstract

Named Entity Recognition (NER) is a tough task in Chinese social media due to a large portion of informal writings. Existing research uses only limited in-domain annotated data and achieves low performance. In this paper, we utilize both limited in-domain data and enough out-of-domain data using a domain adaptation method. We propose a multichannel LSTM-CRF model that employs different channels to capture general patterns, in-domain patterns and out-of-domain patterns in Chinese social media. The extensive experiments show that our model yields 9.8% improvement over previous state-of-the-art methods. We further find that a shared embedding layer is important and randomly initialized embeddings are better than the pretrained ones.

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Notes

  1. 1.

    Entities V1.7, Linguistic Data Consortium, 2014.

  2. 2.

    We just fix four obvious annotating errors with starting PER character tagged as ‘I-PER’ in the training set.

  3. 3.

    https://radimrehurek.com/gensim/index.html.

  4. 4.

    [26, 27] update their results here http://www.cs.jhu.edu/~npeng/papers/golden_horse_supplement.pdf.

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Acknowledgments

The research work has been supported by the Natural Science Foundation of China under Grant No. 61403379 and No. 61402478.

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Authors and Affiliations

  1. CASIA, National Laboratory of Pattern Recognition, Beijing, China

    Chuanhai Dong, Huijia Wu, Jiajun Zhang & Chengqing Zong

  2. University of Chinese Academy of Sciences, Beijing, China

    Chuanhai Dong, Huijia Wu, Jiajun Zhang & Chengqing Zong

  3. CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China

    Chengqing Zong

Authors
  1. Chuanhai Dong
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  2. Huijia Wu
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  3. Jiajun Zhang
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  4. Chengqing Zong
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Corresponding author

Correspondence to Chuanhai Dong .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Maosong Sun

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Xiaojie Wang

  3. Peking University, Beijing, China

    Baobao Chang

  4. Soochow University, Suzhou, China

    Deyi Xiong

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Dong, C., Wu, H., Zhang, J., Zong, C. (2017). Multichannel LSTM-CRF for Named Entity Recognition in Chinese Social Media. In: Sun, M., Wang, X., Chang, B., Xiong, D. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2017 2017. Lecture Notes in Computer Science(), vol 10565. Springer, Cham. https://doi.org/10.1007/978-3-319-69005-6_17

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  • DOI: https://doi.org/10.1007/978-3-319-69005-6_17

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