[1] BUNESCU R, MOONEY R. A shortest path dependency kernel for relation extraction[C]//Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, 2005: 724-731.
[2] RIEDEL S, YAO L, MCCALLUM A, et al. Relation extraction with matrix factorization and universal schemas[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2013: 74-84.
[3] CHEN Y, XU L, LIU K, et al. Event extraction via dynamic multi-pooling convolutional neural networks[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, 2015: 167-176.
[4] DIEFENBACH D, LOPEZ V, SINGH K, et al. Core techniques of question answering systems over knowledge bases: A survey[J]. Knowledge and Information Systems, 2018, 55(3): 529-569.
[5] ZHANG Y, YANG J. Chinese NER using lattice LSTM[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018: 1554-1564.
[6] 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.
[7] ZHU Y, WANG G, KARLSSON B F. CAN-NER: Convolutional attention network for Chinese named entity recognition[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019: 3384-3393.
[8] MENGGE X, BOWEN Y, TINGWEN L, et al. Porous lattice-based transformer encoder for Chinese NER[C]//Proceedings of the 28th International Conference on Computational Linguistics, 2020: 3831-3841.
[9] LI X, YAN H, QIU X, et al. FLAT: Chinese NER using flat-lattice transformer[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020: 6836-6842.
[10] JIANG W, WANG Y, TANG Y. SDTCNs: Asymmetric double temporal convolutional network for Chinese NER[C]//Proceedings of the International Conference on Wireless Algorithms, Systems, and Applications, 2020: 194-205.
[11] HU Y, ZHU Y, ZHANG J, et al.Toward fully exploiting heterogeneous corpus: A decoupled named entity recognition model with two-stage training[C]//Proceedings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 2021: 1641-1652.
[12] 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, 2019: 4171-4186.
[13] MA R, PENG M, ZHANG Q, et al. Simplify the usage of lexicon in Chinese NER[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2019: 5951-5960.
[14] DONG C, ZHANG J, ZONG C, et al. Character-based LSTM-CRF with radical-level features for Chinese named entity recognition[M]//Natural Language Understanding and Intelligent Applications.Cham: Springer, 2016: 239-250.
[15] BAI S, KOLTER J Z, KOLTUN V. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling[J]. arXiv preprint arXiv: 1803.01271, 2018.
[16] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7132-7141.
[17] MENG Y, WU W, WANG F, et al. Glyce: Glyph-vectors for chinese character representations[C]//Proceedings of the 33rd Conference on Neural Information Processing Systems, 2019, 32: 2746-2757.
[18] WU S, SONG X, FENG Z. Mect: Multi-metadata embedding based cross-transformer for Chinese named entity recognition[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021: 1529-1539.
[19] SRIVASTAVA R K, GREFF K, SCHMIDHUBER J. Training very deep networks[C]//Proceedings of the 29th Annual Conference on Neural Information Processing Systems, 2015: 2377-2385.
[20] YUAN M, LI Y. Bi-Lattice LSTM model with self-attention for Chinese NER[C]//Proceedings of the IEEE 20th International Conference on Communication Technology, 2020: 1465-1470.
[21] JIN Y, XIE J, GUO W, et al. LSTM-CRF neural network with gated self attention for Chinese NER[J]. IEEE Access, 2019, 7: 136694-136703.
[22] 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.
[23] 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.
[24] HE H, SUN X. F-score driven max margin neural network for named entity recognition in Chinese social media[C]//Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, 2017: 713-718.
[25] LEVOW G A. The third international Chinese language processing bakeoff: Word segmentation and named entity recognition[C]//Proceedings of the15th SIGHAN Workshop on Chinese Language Processing, 2006: 108-117.
[26] WEISCHEDEL R, PRADHAN S, RAMSHAW L, et al. Ontonotes release 4.0[DS]. LDC2011T03, Philadelphia, Penn.: Linguistic Data Consortium, 2011.
[27] 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.
[28] HE H, SUN X. A unified model for cross-domain and semi-supervised named entity recognition in Chinese social media[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2017: 3216-3222.
[29] 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.
[30] CHEN A, PENG F, SHAN R, et al. Chinese named entity recognition with conditional probabilistic models[C]//Proceedings of the 15th SIGHAN Workshop on Chinese Language Processing, 2006: 173-176.
[31] ZHANG S, QIN Y, HOU W J, et al. Word segmentation and named entity recognition for sighan bakeoff 3[C]//Proceedings of the 15th SIGHAN Workshop on Chinese Language Processing, 2006: 158-161.
[32] ZHOU J, QU W, ZHANG F. Chinese named entity recognition via joint identification and categorization[J]. Chinese Journal of Electronics, 2013, 22(2): 225-230.
[33] 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.
[34] CHE W, WANG M, MANNING C D, et al. Named entity recognition with bilingual constraints[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2013: 52-62.
[35] WANG M, CHE W, MANNING C. Effective bilingual constraints for semi-supervised learning of named entity recognizers[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2013: 919-925.
[36] YANG J, TENG Z, ZHANG M, et al. Combining discrete and neural features for sequence labeling[C]//Proceedings of the International Conference on Intelligent Text Processing and Computational Linguistics, 2016: 140-154.