Deep learning has achieved remarkable success in a wide range of domains. However, it has not been comprehensively evaluated as a solution for the task of Chinese biomedical named entity recognition (Bio-NER). The traditional deep-learning approach for the Bio-NER task is usually based on the structure of recurrent neural networks (RNN) and only takes word embeddings into consideration, ignoring the value of character-level embeddings to encode the morphological and shape information. We propose an RNN-based approach, WCP-RNN, for the Chinese Bio-NER problem. Our method combines word embeddings and character embeddings to capture orthographic and lexicosemantic features. In addition, POS tags are involved as a priori word information to improve the final performance. The experimental results show our proposed approach outperforms the baseline method; the highest F-scores for subject and lesion detection tasks reach 90.36 and 90.48% with an increase of 3.10 and 2.60% compared with the baseline methods, respectively.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Yang J-J, Li J, Mulder J, Wang Y, Chen S, Wu H, Wang Q, Pan H (2015) Emerging information technologies for enhanced healthcare. Comput Ind 69:3–11
Zhang S, Elhadad N (2013) Unsupervised biomedical named entity recognition: experiments with clinical and biological texts. J Biomed Inform 46(6):1088–1098
Mao R, Xu H, Wu W, Li J, Li Y, Lu M (2015) Overcoming the challenge of variety: big data abstraction, the next evolution of data management for AAL communication systems. IEEE Commun Mag 53(1):42–47
Lafferty J, McCallum A, Pereira FCN (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Enabling recognition of diseases in biomedical text with machine learning: corpus and benchmark
Tsochantaridis I, Joachims T, Hofmann T, Altun Y (2005) Large margin methods for structured and interdependent output variables. J Mach Learn Res 6(Sep):1453–1484
Mao R, Zhang P, Li X, Liu X, Lu M (2016) Pivot selection for metric-space indexing. Int J Mach Learn Cybern 7(2):311–323
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444
Tompson JJ, Jain A, LeCun Y, Bregler C (2014) Joint training of a convolutional network and a graphical model for human pose estimation. In: Advances in neural information processing systems. pp 1799–1807
Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P (2011) Natural language processing (almost) from scratch. J Mach Learn Res 12(Aug):2493–2537
Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems. pp 3111–3119
Socher R, Perelygin A, Wu J, Chuang J, Manning CD, Ng A, Potts C (2013) Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. pp 1631–1642
Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078
Seok M, Song H-J, Park C-Y, Kim J-D, Kim Y-S (2016) Named entity recognition using word embedding as a feature. Int J Softw Eng Appl 10(2):93–104
Sahu SK, Anand A (2016) Recurrent neural network models for disease name recognition using domain invariant features. arXiv preprint arXiv:1606.09371
Tang B, Cao H, Wang X, Chen Q, Xu H (2014) Evaluating word representation features in biomedical named entity recognition tasks. BioMed Res Int 2014:240403
Li C, Song R, Liakata M, Vlachos A, Seneff S, Zhang X (2015) Using word embedding for bio-event extraction. In: Proceedings of the 2015 Workshop on Biomedical Natural Language Processing (BioNLP 2015). Association for Computational Linguistics, Stroudsburg, pp 121–126
Nie Y, Rong W, Zhang Y, Ouyang Y, Xiong Z (2015) Embedding assisted prediction architecture for event trigger identification. J Bioinform Comput Biol 13(03):1541001
Jagannatha AN, Yu H (2016) Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter. Meeting, 2016, 473. NIH Public Access
Jagannatha AN, Yu H (2016) Structured prediction models for rnn based sequence labeling in clinical text. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2016, 856. NIH Public Access
Lei J, Tang B, Lu X, Gao K, Jiang M, Xu H (2013) A comprehensive study of named entity recognition in chinese clinical text. J Am Med Inform Assoc 21(5):808–814
Yan Y, Wen D, Wang Y, Wang K (2014) Named entity recognition in chinese medical records based on cascaded conditional random field. J Jilin Univers Eng Technol Edn 6:048
Dong X, Qian L, Guan Y, Huang L, Yu Q, Yang J (2016) A multiclass classification method based on deep learning for named entity recognition in electronic medical records. In: Scientific data summit (NYSDS). IEEE, New York, pp 1–10
Wu Y, Jiang M, Lei J, Xu H (2015) Named entity recognition in chinese clinical text using deep neural network. Stud Health Technol Inform 216:624
Botha J, Blunsom P (2014) Compositional morphology for word representations and language modelling. In: International Conference on Machine Learning. pp 1899–1907
Chen X, Xu L, Liu Z, Sun M, Luan H-B (2015) Joint learning of character and word embeddings. In: IJCAI. pp 1236–1242
Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw 5(2):157–166
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw 18(5):602–610
Dyer C, Ballesteros M, Ling W, Matthews A, Smith NA (2015) Transition-based dependency parsing with stack long short-term memory. arXiv preprint arXiv:1505.08075
Cho K, Van Merriënboer B, Bahdanau D, Bengio Y (2014) On the properties of neural machine translation: encoder-decoder approaches. arXiv preprint arXiv:1409.1259
Kinga D, Adam JB (2015) A method for stochastic optimization. In: International Conference on Learning Representations (ICLR)
This work is supported by Beijing Natural Science Foundation (4152007) and China National Key Technology Research and Development Program Project with No. 2015BAH13F01.
About this article
Cite this article
Li, J., Zhao, S., Yang, J. et al. WCP-RNN: a novel RNN-based approach for Bio-NER in Chinese EMRs. J Supercomput (2018). https://doi.org/10.1007/s11227-017-2229-x
- RNN-based model
- POS tags
- Chinese EMRs