Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
DCA: Diversified Co-attention Towards Informative Live Video Commenting Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Incorporating Lexicon for Named Entity Recognition of Traditional Chinese Medicine Books

Authors : Bingyan Song, Zhenshan Bao, YueZhang Wang, Wenbo Zhang, Chao Sun

Published in: Natural Language Processing and Chinese Computing

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Little research has been done on the Named Entity Recognition (NER) of Traditional Chinese Medicine (TCM) books and most of them use statistical models such as Conditional Random Fields (CRFs). However, in these methods, lexicon information and large-scale of unlabeled corpus data are not fully exploited. In order to improve the performance of NER for TCM books, we propose a method which is based on biLSTM-CRF model and can incorporate lexicon information into representation layer to enrich its semantic information. We compared our approach with several previous character-based and word-based methods. Experiments on “Shanghan Lun” dataset show that our method outperforms previous models. In addition, we collected 376 TCM books to construct a large-scale of corpus to obtain the pre-trained vectors since there is no large available corpus in this field before. We have released the corpus and pre-trained vectors to the public.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Feature Store for Enhanced Explainability in Support Ticket Classification
next chapter Anaphora Resolution in Chinese for Analysis of Medical Q&A Platforms
Footnotes
Literature
1.
Tang, J., Bao, Y.: Traditional Chinese medicine. Lancet. 372(9654), 1938-1940 (2008)
2.
Meng, H., Xie, Q.: Automatic identification of TCM terminology in Shanghan Lun based on Conditional Random Field. J. Beijing Univ. Tradit. Chin. 38(9), 587–590 (2015)
3.
Ye, H., Ji, D.: Research on symptom and medicine information abstraction of TCM book Jin Gui Yao Lue based on conditional random field. Chin. J. Libr. Inf. Sci. Tradit. Chin. Med. 040(005), 14–17 (2016)
4.
Wang, G., Du, J.: POS tagging and feature recombination for ancient prose of TCM diagnosis. Comput. Eng. Design 3, 835–840 (2015)
5.
Li, M., Liu, Z.: LSTM-CRF based symptom term recognition on traditional Chinese medical case. J. Comput. Appl. 38(3), 835–840 (2018)
6.
Zhang, Y., Jie, Y.: Chinese NER using lattice LSTM. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL, Melbourne, pp. 1554–1564 (2018)
7.
Ma, R., Peng, M., Zhang, Q., et al.: Simplify the usage of lexicon in Chinese NER. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5951–5960 (2020)
8.
Wang, Q., Zhou, Y., Ruan, T., et al.: Incorporating dictionaries into deep neural networks for the Chinese clinical named entity recognition. J. Biomed. Inform. 92, 103133 (2019)CrossRef
9.
Lu, N., Zheng, J., Wu, W., et al.: Chinese clinical named entity recognition with word-level information incorporating dictionaries. In: Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2019)
10.
Guo, J., Che, W., Wang, H., Liu, T.: Revisiting embedding features for simple semi-supervised learning. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), ACL, Stroudsburg, pp. 110–120 (2014)
11.
Mnih, A., Hinton, G.E.: A scalable hierarchical distributed language model. In: Advances in Neural Information Processing Systems, pp. 1081–1088 (2009)
12.
Collobert, R., Weston, J.: A unified architecture for natural language processing: Deep neural networks with multitask learning. In: Proceedings of the 25th international conference on Machine learning, pp. 160–167 (2008)
13.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
14.
Siwei, L., Kang, L., Shizhu, H.: How to generate a good word embedding. IEEE Intell. Syst. 31(6), 5–14 (2016)CrossRef
15.
Greenberg, N., Bansal, T., Verga, P., McCallum, A.: Marginal likelihood training of BiLSTM-CRF for biomedical named entity recognition from disjoint label sets. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2824–2829 (2018)
16.
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRef
17.
Lafferty, J.D., Mccallum, A., Pereira F.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Eighteenth International Conference on Machine Learning, pp. 282–289. Morgan Kaufmann Publishers Inc. (2001)
18.
Metadata
Title
Incorporating Lexicon for Named Entity Recognition of Traditional Chinese Medicine Books
Authors
Bingyan Song
Zhenshan Bao
YueZhang Wang
Wenbo Zhang
Chao Sun
Copyright Year
2020
Book
Natural Language Processing and Chinese Computing

Print ISBN: 978-3-030-60456-1

Electronic ISBN: 978-3-030-60457-8

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-60457-8_39

Premium Partner

    Image Credits