[1] SANG E T K, DEMEULDER F. Introduction to the CoNLL-2003 shared task:Language-independent named entity recognition[C]//Proceedings of the 7th Conference on Natural Language Learning at HLT-NAACL, 2003: 142-147.
[2] DODDINGTON G R, MITCHELL A,PRZYBOCKI M, et al. The automatic content extraction (ace) program-tasks, data, and evaluation[C]//Proceedings of the 40th International Conference on Language Resources and Evaluation, 2000,2(1): 837-840.
[3] DEMARTINI G, IOFCIU T, DE VRIES A P. Overview of the INEX entity ranking track[C]//Proceedings of the International Workshop of the Initiative for the Evaluation of XML Retrieval. Springer, Berlin, Heidelberg, 2009: 254-264.
[4] BALOG K, SERDYUKOV P, D E VRIES A P. Overview of the TREC entity track[C]//Proceedings of the TREC. 2011, 2011:11.
[5] BIKEL D. M, MILLER S, SCHWARTZ R, et al. Nymble: A high-performance learning name-finder[C]// Proceedings of the 5th Conference on Applied Natural Language Processing, 1997: 194-201.
[6] ISOZAKI H, KAZAWA H. Efficient support vector classifiers for named entity recognition[C]//Proceedings of the COLING: The 19th International Conference on Computational Linguistics, 2002.
[7] LAFFERTY J, MCCALLUM A, PEREIRA F. Conditional random fields: Probabilistic models for segmenting and labeling sequence data[C]//Proceedings of 18th International Conference on Machine Learning, 2001: 282-289.
[8] LAMPLE G, BALLESTEROS M, SUBRAMANIAN S, et al. Neural architectures for named entity recognition[C]//Proceedings of NAACL-HLT, 2016: 260-270.
[9] PENG N, DREDZE M. Improving named entity recognition for chinese social media with word segmentation representation learning[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2016: 149-155.
[10] LUO W, YANG F. An empirical study of automatic Chinese word segmentation for spoken language understanding and named entity recognition[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016: 238-248.
[11] CAO P, CHEN Y, LIU K, et al. Adversarial transfer learning for Chinese named entity recognition with self-attention mechanism[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2018: 182-192.
[12] WANG M, CHE W, MANNING C D. Joint word alignment and bilingual named entity recognition using dual decomposition[C]//Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, 2013: 1073-1082.
[13] LI J, SUN A, HAN J, et al. A survey on deep learning for named entity recognition[J]. IEEE Transactions on Knowledge and Data Engineering, 2020,34(1): 50-70.
[14] SZARVAS G, FARKAS R, KOCSOR A. A multilingual named entity recognition system using boosting and c4. 5 decision tree learning algorithms[C]//Proceedings of the International Conference on Discovery Science. Springer, Berlin, Heidelberg, 2006: 267-278.
[15] BIKEL D M, SCHWARTZ R, WEISCHEDEL R M. An algorithm that learns what's in a name[J]. Machine Learning, 1999, 34(1): 211-231.
[16] MA X, HOVY E. End-to-end sequence labeling via Bi-directional LSTM-CNNs-CRF[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2016: 1064-1074.
[17] KURU O, CAN O A,YURET D. Charner: Character-level named entity recognition[C]//Proceedings of COLING, the 26th International Conference on Computational Linguistics: Technical Papers, 2016: 911-921.
[18] COLLOBERT R, WESTON J, BOTTOU L, et al. Natural language processing (almost) from scratch[J]. Journal of Machine Learning Research, 2011, 12: 2493-2537.
[19] REI M. Semi-supervised multitask learning for sequence labeling[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017: 2121-2130.
[20] LIN Y, YANG S,STOYANOV V, et al. A multi-lingual multi-task architecture for low-resource sequence labeling[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018: 799-809.
[21] CRICHTON G, PYYSALO S, CHIU B, et al. A neural network multi-task learning approach to biomedical named entity recognition[J]. BMC Bioinformatics, 2017, 18(1): 1-14.
[22] WANG X, ZHANG Y, REN X, et al. Cross-type biomedical named entity recognition with deep multi-task learning[J]. Bioinformatics, 2018, 1: 8.
[23] LI H, HAGIWARA M, LI Q, et al. Comparison of the impact of word segmentation on name tagging for chinese and japanese[C]//Proceedings of the LREC, 2014: 2532-2536.
[24] HUANG Z, WEI X, KAI Y. Bidirectional LSTM-CRF models for sequence tagging[J]. arXiv preprint arXiv:1508.01991, 2015.
[25] LU Y, ZHANG Y, JI D. Multi-prototype Chinese character embedding[C]//Proceedings of the 10th International Conference on Language Resources and Evaluation, 2016: 855-859.
[26] ZHANG Y, YANG J. Chinese NER using lattice LSTM[C]//Proceedings of the 56th Annual Meeting of the Association for ComputationalLinguistics, 2018: 1554-1564.
[27] ZHOU P, ZHENG S, XU J, et al. Joint extraction of multiple relations and entities by using a hybrid neural network[M]. Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. Springer, Cham, 2017: 135-146.
[28] PENG N, DREDZE M. Named entity recognition for Chinese social media with jointly trained embeddings[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2015: 548-554.
[29] HE H, SUN X. F-score driven max margin neural network for named entity recognition in chinesesocial media[C]//Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, 2017: 713-718.
[30] DEVLIN J, CHANG M W, LEE K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019: 4171-4186.
[31] RANA R. Gated recurrent unit (GRU) for emotion classification from noisyspeech[J/ OL]. arXiv preprint arXiv:1612.07778, 2016.
[32] MA X, HOVY E. End-to-end sequence labeling via Bi-directional LSTM-CNNs-CRF[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2016: 1064-1074.
[33] KINGMA D, BA J. Adam: A method for stochastic optimization[C]//Proceedings of the ICLR, 2015.
[34] HINTON G, SRIVASTAVE N, KRIZHEVSKY A, et al. Improving neural networks by preventing co-adaptation of feature detectors.[J/ OL].arXiv preprint arXiv: 1207.0580, 2012.
[35] SHEN Y, YUN H, LIPTON Z C, et al. Deep active learning for named entity recognition[C]//Proceedings of the 2nd Workshop on Representation Learning for NLP, 2017: 252-256.