李源,马磊,邵党国,袁梅宇,张名芳. 用于社交媒体的中文命名实体识别[J]. 中文信息学报, 2020, 34(8): 61-69.
LI Yuan, MA Lei, SHAO Dangguo, YUAN Meiyu, ZHANG Mingfang. Chinese Named Entity Recognition for Social Media. , 2020, 34(8): 61-69.
用于社交媒体的中文命名实体识别
李源,马磊,邵党国,袁梅宇,张名芳
昆明理工大学 信息工程与自动化学院,云南 昆明 650504
Chinese Named Entity Recognition for Social Media
LI Yuan, MA Lei, SHAO Dangguo, YUAN Meiyu, ZHANG Mingfang
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan 650504, China
Abstract:Chinese named entity recognition (NER) in social media is a challenging task. Existing methods based on word-level information or external knowledge are affected by Chinese word segmentation (CWS) and Out-of-Vocabulary (OOV). This paper proposes an adversarial learning model based on character using positional encoding and multi-attention. The combination of positional encoding and self-attention can better capture the dependence of character sequences, while the use of spatial attention discriminator can improve the extraction effect of external knowledge. The experimental results show that the proposed approach achieves 56.79% and 60.62% in F-score, respectively, on the datasets in Weibo2015 and Weibo2017.
[1] Chinchor N.MUC-6 named entity task definition[C]//Proceedings of the 6th Conference on Message Understanding, 1995. [2] Fukuda K, Tsunoda T, Tamura A, et al. Toward information extraction: identifying protein names from biological papers[C]//Proceedings of the Pacific Symposium on Biocomputing. 1998. [3] Rindflesch T C, Tanabe L, Weinstein J N, et al. EDGAR: Extraction of Drugs, Genes and Relations from the Biomedical Literature[C]//Proceedings of the Pacific Symposium on Biocomputing. 2000. [4] Nanyun Peng, Mark Dredze. Named entity recognition for chinese social media with jointly trained embeddings[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015: 548-554. [5] Zhiheng Huang, Wei Xu, Kai Yu. Bidirectional LSTM-CRF models for sequence tagging [J]. arXiv preprint arXiv: 1508.01991, 2015. [6] Nanyun Peng, Mark Dredze. 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. [7] Xiaoya Li, Yuxian Meng, Xiaofei Sun, et al. Is Word Segmentation Necessary for Deep Learning of Chinese Representations? [C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, ACL, 2019:3242-3252. [8] Yue Zhang, Jie Yang. Chinese NER using lattice LSTM[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL,2018, 1:1554-1564. [9] Yuying Zhu, Guoxin Wang. CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, MN, USA ,2019 ,1:3384-3393. [10] Pengfei Cao, Yubo Chen, Kang Liu, et al. Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 2018: 182-192. [11] Wren S. The Cognitive Foundations of Learning to Read : A Framework, ED448420 [R]. Washington, DC:Office of Educational Research and Improvement, 2001. [12] Ashish Vaswani, Noam Shazeer, Niki Parmar, et al. Attention is All you Need[J]. arXiv preprint arXiv: 1706.03762, 2017. [13] Mike Schuster, Kuldip K. Paliwal. Bidirectional recurrent neural networks[J]. IEEE Transactions on Signal Processing, 1997, 45(11):2673-2681. [14] John Lafferty, Andrew McCallum, Fernando CN Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data[C]//Proceedings of the 18th International Conference on Machine Learning, Williams College, Williamstown, MA, USA, 2001: 282-289. [15] Sanghyun Woo, Jongchan Park, Joon Young Lee. CBAM: Convolutional Block Attention Module[C]//Proceedings of the European Conference on Computer Vision, 2018: 3-19. [16] Goodfellow I J, Pouget Abadie J, Mirza M, et al. Generative Adversarial Nets[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems. 2014. [17] Xinchi Chen, XipengQiu, Chenxi Zhu, et al. Long short-term memory neural networks for chinese word segmentation[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2015: 1197-1206. [18] Hangfeng He, Xu Sun. 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. [19] Hu Xu, Bing Liu, Lei Shu, et al. Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, 2018,2:592-598.