• Akbik A, Blythe D, Vollgraf R, 2018. Contextual string embeddings for sequence labeling. Proc 27^th Int Conf on Computational Linguistics, p.1638–1649.

• Chang CC, Lin CJ, 2011. LIBSVM—a library for support vector machines.ACM Trans Intell Syst Technol, 2, Article 27.https://doi.org/10.1145/1961189.1961199

• Chawla NV, Bowyer KW, Hall LO, et al., 2002. SMOTE: synthetic minority over-sampling technique.J Artif Intell Res, 16:321–357.https://doi.org/10.1613/jair.953

  Article  Google Scholar 

• Chiu JPC, Nichols E, 2016. Named entity recognition with bidirectional LSTM-CNNs.Trans Assoc Comput Ling, 4:357–370.https://doi.org/10.1162/tacl_a_00104

  Google Scholar 

• Collobert R, Weston J, Bottou L, et al., 2011. Natural language processing (almost) from scratch.J Mach Learn Res, 12:2493–2537.

  MATH  Google Scholar 

• Cortes C, Vapnik V, 1995. Support-vector networks.Mach Learn, 20(3):273–297.https://doi.org/10.1007/BF00994018

  MATH  Google Scholar 

• Devlin J, Chang MW, Lee K, et al., 2018. BERT: pretraining of deep bidirectional transformers for language understanding.https://arxiv.org/abs/1810.04805

• Ghaddar A, Langlais P, 2018. Robust lexical features for improved neural network named-entity recognition. Proc 27^th Int Conf on Computational Linguistics, p.1896–1907.

• Grandvalet Y, Bengio Y, 2006. Entropy regularization. In: Chapelle O, Schölkopf B, Zien A (Eds.), Semisupervised Learning. MIT Press, Cambridge, Mass, p.151–168.https://doi.org/10.7551/mitpress/9780262033589.001.0001

  Google Scholar 

• Hochreiter S, Schmidhuber J, 1997. Long short-term memory.Neur Comput, 9(8):1735–1780.https://doi.org/10.1162/neco.1997.9.8.1735

  Article  Google Scholar 

• Hu J, Shi X, Liu Z, et al., 2017. HITSZ_CNER: a hybrid system for entity recognition from Chinese clinical text. China Conf on Knowledge Graph and Semantic Computing, p.1–6.

• Huang Z, Xu W, Yu K, 2015. Bidirectional LSTM-CRF models for sequence tagging.https://arxiv.org/abs/1508.01991

• Jagannatha AN, Yu H, 2016. Structured prediction models for RNN based sequence labeling in clinical text. Proc Conf on Empirical Methods in Natural Language Processing, p.856.https://doi.org/10.18653/v1/D16-1082

• Lafferty JD, McCallum A, Pereira FCN, 2001. Conditional random fields: probabilistic models for segmenting and labeling sequence data. Proc 18^th Int Conf on Machine Learning, p.282–289.

• Lample G, Ballesteros M, Subramanian S, et al., 2016. Neural architectures for named entity recognition. North American Chapter of the Association for Computational Linguistics, p.260–270.https://doi.org/10.18653/v1/N16-1030

• Lee DH, 2013. Pseudo-label: the simple and efficient semisupervised learning method for deep neural networks. Work Shop on Challenges in Representation Learning, p.1–6.

• Li PH, Dong RP, Wang YS, et al., 2017. Leveraging linguistic structures for named entity recognition with bidirectional recursive neural networks. Proc Conf on Empirical Methods in Natural Language Processing, p.2664–2669.https://doi.org/10.18653/v1/D17-1282

• Liao WH, Veeramachaneni S, 2009. A simple semi-supervised algorithm for named entity recognition. Proc NAACL HLT Workshop on Semi-supervised Learning for Natural Language Processing, p.58–65.

• Ma XZ, Hovy E, 2016. End-to-end sequence labeling via bidirectional LSTM-CNNs-CRF. Proc 54^th Annual Meeting of the Association for Computational Linguistics, p.1064–1074.https://doi.org/10.13140/RG.2.1.2182.5685

• Mesnil G, He X, Deng L, et al., 2013. Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding. Interspeech, p.1–5.

• Mikolov T, Sutskever I, Chen K, et al., 2013. Distributed representations of words and phrases and their compositionality. Proc 26^th Int Conf on Neural Information Processing Systems, p.3111–3119.

• Pennington J, Socher R, Manning CD, 2014. Glove: global vectors for word representation. Proc Empirical Methods in Natural Language Processing, p.1532–1543.

• Peters ME, Ammar W, Bhagavatula C, et al., 2017. Semisupervised sequence tagging with bidirectional language models. Proc 55^th Annual Meeting of the Association for Computational Linguistics, p.1756–1765.https://doi.org/10.18653/v1/P17-1161

• Peters ME, Neumann M, Iyyer M, et al., 2018. Deep contextualized word representations.https://arxiv.org/abs/1802.05365

• Pradhan S, Moschitti A, Xue N, et al., 2013. Towards robust linguistic analysis using ontonotes. Proc 7^th Conf on Computational Natural Language Learning, p.143–152.

• Qi YJ, Collobert R, Kuksa P, et al., 2009. Combining labeled and unlabeled data with word-class distribution learning. Proc 18^th ACM Conf on Information and Knowledge Management, p.1737–1740.https://doi.org/10.1145/1645953.1646218

• Rei M, 2017. Semi-supervised multitask learning for sequence labeling. 55^th Annual Meeting of the Association for Computational Linguistics, p.2121–2130.https://doi.org/10.18653/v1/P17-1194

• Schmidhuber J, 2015. Deep learning in neural networks: an overview.Neur Netw, 61:85–117.https://doi.org/10.1016/j.neunet.2014.09.003

  Article  Google Scholar 

• Shen YY, Yun H, Lipton ZC, et al., 2017. Deep active learning for named entity recognition.https://arxiv.org/abs/1707.05928

• Strubell E, Verga P, Belanger D, et al., 2017. Fast and accurate entity recognition with iterated dilated convolutions. Proc Conf on Empirical Methods in Natural Language Processing, p.2670–2680.

• Sun YQ, Li L, Xie ZW, et al., 2017. Co-training an improved recurrent neural network with probability statistic models for named entity recognition. Int Conf on Database Systems for Advanced Applications, p.545–555.https://doi.org/10.1007/978-3-319-55699-4_33

• Tjong Kim Sang EF, de Meulder F, 2003. Introduction to the CoNLL-2003 shared task: language-independent named entity recognition. Proc 7^th Conf on Natural Language Learning at HLT-NAACL, p.142–147.https://doi.org/10.3115/1119176.1119195

• Wu H, Prasad S, 2018. Semi-supervised deep learning using pseudo labels for hyperspectral image classification.IEEE Trans Image Process, 27(3):1259–1270.https://doi.org/10.1109/TIP.2017.2772836

  Article MathSciNet  Google Scholar 

• Xia Y, Wang Q, 2017. Clinical named entity recognition: ECUST in the CCKS-2017 shared task 2. CEUR Workshop Proc, p.43–48.

• Xiao Y, Wang Z, 2017. Clinical Named Entity Recognition Evaluation Tasks at CCKS 2017.http://ceur-ws.org/Vol-1976/

• Yang J, Zhang Y, 2018. NCRF++: an open-source neural sequence labeling toolkit. Proc 56^th Annual Meeting of the Association for Computational Linguistics, p.74–79.http://aclweb.org/anthology/P18-4013

• Zhai F, Potdar S, Xiang B, et al., 2017. Neural models for sequence chunking. Proc 31^st AAAI Conf on Artificial Intelligence, p.3365–3371.

Download references