Open Access
MATEC Web Conf.
Volume 336, 2021
2020 2nd International Conference on Computer Science Communication and Network Security (CSCNS2020)
Article Number 06021
Number of page(s) 8
Section Artificial Recognition and Application
Published online 15 February 2021
  1. Liu L, Dongbo W. A Survey of Named Entity Recognition Research[J].Journal of Information,2018, 37(3):329-340. [Google Scholar]
  2. Carol F, Alderson P.O, Austin J H M, et al. A general natural-language text processor for clinical radiology.[J]. Journal of the American Medical Informatics Association, 1994, 1(2):161-174. [Google Scholar]
  3. R. Gaizauskas, G. Demetriou, and K. Humphreys. Term recognition and classification in biological science journal articles.In Computational Terminology for Medical &Biological Applications Work shop of the 2nd International Conference on NLP[C],2000,pp.37-44. [Google Scholar]
  4. Collobert R, Weston J, Bottou, Léon, et al. Natural Language Processing (almost) from Scratch[J]. Journal of Machine Learning Research, 2011, 12(1):2493-2537. [Google Scholar]
  5. Huang Z, Xu W, Yu K. Bidirectional LSTM-CRF Models for Sequence Tagging[J]. Computerence, 2015 [Google Scholar]
  6. Ma X, Hovy E. End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF[J]. 2016 [Google Scholar]
  7. Ling L, Zhihao Y, Pei Y, et al. An Attention-based BiLSTM-CRF Approach to Document-level Chemical Named Entity Recognition[J].. Bioinformatics(8):8.2019 [Google Scholar]
  8. Wu FZ, Liu JX, Wu CH, et al. Neural Chinese named entity recognition via CNN-LSTM-CRF and joint training with word segmentation. The World Wide Web Conference[C]. New York, NY, USA. 2019. 3342-3348. [Google Scholar]
  9. Peters M.E, Ammar W, Bhagavatula C, et al. Semi-supervised sequence tagging with bidirectional language models[J]. 2019 [Google Scholar]
  10. Jana Straková, Straka M, Haji J. Neural Architectures for Nested NER through Linearization[C]// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019 [Google Scholar]
  11. Devlin J, Chang M.W, Lee K, et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[J]. 2018 [Google Scholar]
  12. Souza, Fábio, Nogueira R, Lotufo R. Portuguese Named Entity Recognition using BERT-CRF[J]. 2020.’ [Google Scholar]
  13. Cui Y, Che W, Liu T, et al. Pre-Training with Whole Word Masking for Chinese BERT[J]. 2019 [Google Scholar]
  14. Hochreiter S, Schmidhuber J. Long Short-Term Memory[J]. Neural Computation, 1997, 9(8):1735-1780. [Google Scholar]
  15. Graves A, Jürgen Schmidhuber. Framewise phoneme classification with bidirectional LSTM and other neural network architectures[J]. Neural Networks, 2005, 18(5-6):602-610. [Google Scholar]
  16. Jia Y.Z, Xu X.B. Chinese named entity recognition based on CNN-BiLSTM-CRF.2018 IEEE 9th International Conference on Software Engineering and Service Science [C].Beijing, China. 2019. 1-4. [Google Scholar]
  17. Xiaojun L, Lichuan G, Xianzhang S. Named entity recognition based on Bi-LSTM and attention mechanism. Journal of Luoyang Institute of Technology (Natural Science Edition)[J], 2019, 29(1): 65-70,77. [Google Scholar]
  18. Zhang Y, Yang J. Chinese NER Using Lattice LSTM[J]. 2018 [Google Scholar]
  19. Li N, Huanmei G, Piao Y, et al. Chinese named entity recognition method based on BERT-IDCNN-CRF. Journal of Shandong University (Science Edition)[J],2020, 55(1):102-109. [Google Scholar]

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