[1] Xie R, Liu Z, Jia J, et al. Representation learning of knowledge graphs with entity descriptions[C]∥Proceedings of the AAAI Conference on Artificial Intelligence, 2016.
[2] Guo J, Xu G, Cheng X, et al. Named entity recognition in query[C]//Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, 2009: 267-274.
[3] Petkova D, Croft W B. Proximity-based document representation for named entity retrieval[C]//Proceedings of the 16th ACM conference on Conference on information and knowledge management, 2007: 731-740.
[4] Mollá D, Van Zaanen M, Smith D. Named entity recognition for question answering[C]//Proceedings of the Australasian Language Technology Workshop 2006, 2006: 51-58.
[5] Babych B, Hartley A. Improving machine translation quality with automatic named entity recognition[C]//Proceedings of the 7th International EAMT workshop on MT and other language technology tools, Improving MT through other language technology tools, Resource and tools for building MT at EACL, 2003.
[6] Sahu S K, Anand A. Recurrent neural network models for disease name recognition using domain invariant features[J]. arXiv preprint arXiv:1606.09371, 2016.
[7] Zhao S, Liu T, Zhao S, et al. A neural multi-task learning framework to jointly model medical named entity recognition and normalization[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2019: 817-824.
[8] Weischedel R, Pradhan S, Ramshaw L, et al. Ontonotes release 4.0[DB]. LDC2011T03, Philadelphia, Penn.: Linguistic Data Consortium, 2011.
[9] Levow G A. The third international Chinese language processing bakeoff: Word segmentation and named entity recognition[C]//Proceedings of the 5th SIGHAN Workshop on Chinese Language Processing, 2006: 108-117.
[10] Jin G, Chen X. The 4th international chinese language processing bakeoff: Chinese word segmentation, named entity recognition and chinese pos tagging[C]//Proceedings of the 6th SIGHAN Workshop on Chinese Language Processing, 2008.
[11] Zhang Y, Yang J.Chinese ner using lattice lstm[J]. arXiv preprint arXiv:1805.02023, 2018.
[12] 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.
[13] He H, Sun X. F-score driven max margin neural network for named entity recognition inchinese social media[J]. arXiv preprint arXiv:1611.04234, 2016.
[14] Ding R,Xie P, Zhang X, et al. A neural multi-digraph model for Chinese NER with gazetteers[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019: 1462-1467.
[15] Sang E F, DeMeulder F. Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition[J]. arXiv preprint cs/0306050, 2003.
[16] Pradhan S, Moschitti A, Xue N, et al. CoNLL-2012 shared task: Modeling multilingual unrestricted coreference in OntoNotes[C]//Proceedings of the Joint Conference on EMNLP and CoNLL-Shared Task, 2012: 1-40.
[17] Lample G, Ballesteros M, Subramanian S, et al. Neural architectures for named entity recognition[J]. arXiv preprint arXiv:1603.01360, 2016.
[18] Zhou J T, Zhang H,Jin D, et al. Roseq: Robust sequence labeling[J]. IEEE Transactions on Neural Networks and Learning Systems, 2019, 31(7): 2304-2314.
[19] Song R, Chen L W, Yuan Q. Person name recognition method based on corpus and rule[C]//Proceedings of the Computational Language Research and Development, 1993.
[20] Tan H Y, Huang C N, Dong Z D. Chinese place automatic recognition research[C]//Proceedings of Computational Language, 1999.
[21] Luo Z, Song R. Integrated and fast recognition of proper noun in modern Chinese word segmentation[C]//Proceedings of International Conference on Chinese Computing, 2001: 323-328.
[22] Fukuda K,Tsunoda T, Tamura A, et al. Toward information extraction: identifying protein names from biological papers[J].Pac symp biocomput, 1998, 707(18): 707-718.
[23] Tsai R T H, Wu S H, Lee C W, et al. Mencius: A Chinese named entity recognizer using the maximum entropy-based hybrid model[J].International Journal of Computational Linguistics and Chinese Language Processing, 2004: 65-82.
[24] Fang X, Sheng H. A hybrid approach forchinese named entity recognition[J]. International Conference on Discovery Science. Springer, 2002: 297-301.
[25] Eddy S R. Hiddenmarkov models[J]. Current opinion in structural biology, 1996, 6(3): 361-365.
[26] Kapur J N. Maximum-entropy models in science and engineering[M]. John Wiley and Sons, 1989.
[27] Quinlan J R. Induction of decision trees[J]. Machine learning, 1986, 1(1): 81-106.
[28] Hearst M A,Dumais S T, Osuna E, et al. Support vector machines[J]. IEEE Intelligent Systems and their applications, 1998, 13(4): 18-28.
[29] Lafferty J, McCallum A, Pereira F C N. Conditional random fields: Probabilistic models for segmenting and labeling sequence data[C]//Proceedings of the 18th International Conference on Machine Learning. 2001: 282-289.
[30] Fu G, Luke K K. Chinese named entity recognition using lexicalized HMMs[J]. ACM SIGKDD Explorations Newsletter, 2005, 7(1): 19-25.
[31] Chen A, Peng F, Shan R, et al. Chinese named entity recognition with conditional probabilistic models[C]//Proceedings of the 5th SIGHAN Workshop on Chinese Language Processing, 2006: 173-176.
[32] Yu X, Lam W, Chan S K, et al. Chinese NER using CRFs and logic for the fourth SIGHAN bakeoff[C]//Proceedings of the 6th SIGHAN Workshop on Chinese Language Processing, 2008.
[33] Zhang Y, Xu Z, Zhang T. Fusion of multiple features forchinese named entity recognition based on CRF model[C]//Proceedings of the 4th Asia Information Retrieval Symposium, 2008: 95-106.
[34] Settles B. Biomedical named entity recognition using conditional random fields and rich feature sets[C]//Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications, 2004: 107-110.
[35] Liu S, Sun Y, Li B, et al. HAMNER: Headword amplified multi-span distantly supervised method for domain specific named entity recognition[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2020: 8401-8408.
[36] 张朝胜,郭剑毅,线岩团,等.基于条件随机场的英文产品命名实体识别[J].计算机工程与科学, 2010, 32(6): 115-117.
[37] 郭剑毅,薛征山,余正涛,等.基于层叠条件随机场的旅游领域命名实体识别[J].中文信息学报, 2009, 23(5): 47-53.
[38] Hammerton J. Named entity recognition with long short-term memory[C]//Proceedings of the 7th Conference on Natural Language Learning, 2003: 172-175.
[39] Cho K, Van Merrinboer B, Bahdanau D, et al. On the properties of neural machine translation: Encoder-decoder approaches[J]. arXiv preprint arXiv:1409.1259, 2014.
[40] Dong C, Zhang J,Zong C, et al. Character-based LSTM-CRF with radical-level features for Chinese named entity recognition[M]. Kunming: Springer, 2016: 239-250.
[41] Jia Y, Ma X. Attention in Character-Based BiLSTM-CRF for Chinese named entity recognition[C]//Proceedings of the 4th International Conference on Mathematics and Artificial Intelligence, 2019: 1-4.
[42] 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.
[43] Wu F, Liu J, Wu C, et al. Neural Chinese named entity recognition via CNN-LSTM-CRF and joint training with word segmentation[C]//Proceedings of the The World Wide Web Conference, 2019: 3342-3348.
[44] Liu W, Xu T, Xu Q, et al. An encoding strategy based word-character LSTM for Chinese NER[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics, 2019: 2379-2389.
[45] Wang Q, Zhou Y,Ruan T, et al. Incorporating dictionaries into deep neural networks for the Chinese clinical named entity recognition[J]. Journal of biomedical informatics, 2019, 92: 103133.
[46] Xu C, Wang F, Han J, et al. Exploiting multiple embeddings for Chinese named entity recognition[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019: 2269-2272.
[47] Zhu Y, Wang G, Karlsson B F. CAN-NER: Convolutional attention network for Chinese named entity recognition[J].arXiv preprint arXiv:1904.02141, 2019.
[48] Collobert R, Weston J. A unified architecture for natural language processing: Deep neural networks with multitask learning[C]//Proceedings of the 25th international conference on Machine learning, 2008: 160-167.
[49] Collobert R, Weston J, Bottou L, et al. Natural language processing (almost) from scratch[J]. Journal of machine learning research, 2011, 12: 2493-2537.
[50] Kalchbrenner N, Grefenstette E, Blunsom P. A convolutional neural network for modelling sentences[J]. arXiv preprint arXiv:1404.2188, 2014.
[51] Kim Y. Convolutional neural networks for sentence classification[J].arXiv preprint arXiv:1408.5882, 2014.
[52] Strubell E, Verga P, Belanger D, et al. Fast and accurate entity recognition with iterated dilated convolutions[J]. arXiv preprint arXiv:1702.02098, 2017.
[53] Gui T, Ma R, Zhang Q, et al. CNN-Based Chinese NER with lexicon rethinking[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence, 2019: 4982-4988.
[54] Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks[J]. arXiv preprint arXiv:1609.02907, 2016.
[55] Velicˇkovic＇ P, Cucurull G, Casanova A, et al. Graph attention networks[J]. arXiv preprint arXiv:1710.10903, 2017.
[56] Li Y,Tarlow D, Brockschmidt M, et al. Gated graph sequence neural networks[J]. arXiv preprint arXiv:1511.05493, 2015.
[57] Sui D, Chen Y, Liu K, et al. Leverage lexical knowledge forchinese named entity recognition via collaborative graph network[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019: 3821-3831.
[58] Gui T, Zou Y, Zhang Q, et al. A lexicon-based graph neural network for Chinese ner[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019: 1039-1049.
[59] Vaswani A,Shazeer N, Parmar N, et al. Attention is all you need[C]//Proceedings of the 31st International Conference on, 2017: 5998-6008.
[60] Li X, Yan H,Qiu X, et al. FLAT: Chinese NER using flat-lattice transformer[J]. arXiv preprint arXiv:2004.11795, 2020.
[61] Mengge X, Bowen Y, Tingwen L, et al. Porous lattice-based transformer encoder for Chinese NER[J]. arXiv preprint arXiv:1911.02733, 2019.
[62] Peng M, Ma R, Zhang Q, et al. Simplify the usage of lexicon in Chinese NER[J].arXiv preprint arXiv:1908.05969, 2019.
[63] Hu M, Peng Y, Huang Z, et al. Open-domain targeted sentiment analysis via span-based extraction and classification[J].arXiv preprint arXiv:1906.03820, 2019.
[64] Sun L, Sun Y, Ji F, et al. Joint learning of token context and span feature for span-based nested NER[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2020, 28: 2720-2730.
[65] Dong X, Xin X, Guo P.Chinese NER by span-level self-attention[C]// Proceedings of the 15th International Conference on Computational Intelligence and Security, 2019: 68-72.
[66] Xu M, Jiang H,Watcharawittayakul S. A local detection approach for named entity recognition and mention detection[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017: 1237-1247.
[67] Sohrab M G, Miwa M. Deep exhaustive model for nested named entity recognition[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2018: 2843-2849.
[68] Mao H, Tang H, Zhang W, et al. A span-based distantly supervised NER with self-learning[C]// Proceedings of the CCF International Conference on Natural Language Processing and Chinese Computing, 2020: 192-203.
[69] Wang S, Jiang J. Machine comprehension using match-lstm and answer pointer[J]. arXiv preprint arXiv:1608.07905, 2016.
[70] Li X, Feng J, Meng Y, et al. A unifiedmrc framework for named entity recognition[J]. arXiv preprint arXiv:1910.11476, 2019.
[71] Li S, Zhao Z, Hu R, et al. Analogical reasoning onchinese morphological and semantic relations[J]. arXiv preprint arXiv:1805.06504, 2018.
[72] Katiyar A, Cardie C. Nested named entity recognition revisited[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics, 2018: 861-871.
[73] Straková J, Straka M, Hajicˇ J. Neural architectures for nested NER through linearization[J]. arXiv preprint arXiv:1908.06926, 2019.
[74] Ju M, Miwa M,Ananiadou S. A neural layered model for nested named entity recognition[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics, 2018: 1446-1459.
[75] Jue W, Shou L, Chen K, et al. Pyramid: A layered model for nested named entity recognition[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020: 5918-5928.
[76] Lin H, Lu Y, Han X, et al. Sequence-to-nuggets: Nested entity mention detection via anchor-region networks[J].arXiv preprint arXiv:1906.03783, 2019.
[77] Zhou G, Zhang J,Su J, et al. Recognizing names in biomedical texts: A machine learning approach[J]. Bioinformatics, 2004, 20(7): 1178-1190.
[78] Zhou G D. Recognizing names in biomedical texts using mutual information independence model and SVM plus sigmoid[J]. International Journal of Medical Informatics, 2006, 75(6): 456-467.
[79] 刘非凡,赵军,徐波.实体提及的多层嵌套识别方法研究[J].中文信息学报, 2007, 21(2): 14-21.
[80] Fu C, Fu G. Morpheme-basedchinese nested named entity recognition[C]//Proceedings of the 8th International Conference on Fuzzy Systems and Knowledge Discovery, 2011, 2: 1221-1225.
[81] Fu C, Fu G. A dual-layer CRFs based method forchinese nested named entity recognition[C]//Proceedings of the 9th International Conference on Fuzzy Systems and Knowledge Discovery, 2012: 2546-2550.
[82] 郭慧志,刘华,谢学敏,等.《人民日报》标注语料的初步统计分析[C]//全国第八届计算语言学联合学术会议(JSCL-2005)论文集, 2005.
[83] 黄鑫,朱巧明,钱龙华,等.基于特征组合的中文实体关系抽取[J].微电子学与计算机, 2010, 27(4): 198-200.
[84] 徐薇,付滨,刘柳,等.中文命名实体识别系统的领域扩展[C]//第9届全国计算语言学学术会议论文集, 2007.
[85] Fu G, Luke K K. Chinese named entity recognition using lexicalized HMMs[J]. ACM SIGKDD Explorations Newsletter, 2005, 7(1): 19-25.
[86] Chen H H, Ding Y W, Tsai S C, et al. Description of the NTU system used for MET-2[C]//Proceedings of the 7th Message Understanding Conference, 1998: 1-9.
[87] 李雁群,何云琪,钱龙华,等.中文嵌套命名实体识别语料库的构建[J].中文信息学报, 2018, 32(8): 19-26.
[88] 李雁群,何云琪,钱龙华,等.基于维基百科的中文嵌套命名实体识别语料库自动构建[J].计算机工程, 2018: 12.
[89] 许浩亮,李雁群,何云琪,等.中文嵌套命名实体关系抽取研究[J].北京大学学报(自然科学版), 2019(01): 8-14.