[1] 殷章志, 李欣子, 黄德根, 等.融合字词模型的中文命名实体识别研究[J].中文信息学报, 2019, 33(11):95-100, 106. YIN Z Z, LI X Z, HUANG D G, et al.Chinese named entity recognition ensembled with character[J].Journal of Chinese Information Processing, 2019, 33(11):95-100, 106.(in Chinese) [2] 王红, 史金钏, 张志伟.基于注意力机制的LSTM的语义关系抽取[J].计算机应用研究, 2018, 35(5):1417-1420, 1440. WANG H, SHI J C, ZHANG Z W.Text semantic relation extraction of LSTM based on attention mechanism[J].Application Research of Computers, 2018, 35(5):1417-1420, 1440.(in Chinese) [3] HUANG Z, XU W, YU K.Bidirectional LSTM-CRF models for sequence tagging[EB/OL].[2021-03-16].https://arxiv.org/abs/1508.01991v1. [4] ZHANG Y, YANG J.Chinese NER using lattice LSTM[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics.Stroudsburg, USA:Association for Computational Linguistics, 2018:1554-1564. [5] 杜琳, 曹东, 林树元, 等.基于BERT与Bi-LSTM融合注意力机制的中医病历文本的提取与自动分类[J].计算机科学, 2020, 47(S2):416-420. DU L, CAO D, LIN S Y, et al.Extraction and automatic classification of TCM medical records based on attention mechanism of BERT and Bi-LSTM[J].Computer Science, 2020, 47(S2):416-420.(in Chinese) [6] ZENG D H, SUN C J, LIN L, et al.LSTM-CRF for drug-named entity recognition[J].Entropy, 2017, 19(6):283. [7] YAN S, CHAI J P, WU L Y.Bidirectional GRU with multi-head attention for Chinese NER[C]//Proceedings of the 5th Information Technology and Mechatronics Engineering Conference.Washington D.C., USA:IEEE Press, 2020:1160-1164. [8] DING R X, XIE P J, ZHANG X Y, 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.Stroudsburg, USA:Association for Computational Linguistics, 2019:1462-1467. [9] VASWANI A, SHAZEER N, PARMAR N, et al.Attention is all you need?[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.New York, USA:ACM Press, 2017:6000-6010. [10] GUO S G, LIU Y P, LI H, et al.Transformer winding deformation detection based on BOTDR and ROTDR[J].Sensors, 2020, 20(7):2062. [11] DAI Z H, YANG Z L, YANG Y M, et al.Transformer-XL:attentive language models beyond a fixed-length context[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.Stroudsburg, USA:Association for Computational Linguistics, 2019:1-15. [12] SHAW P, USZKOREIT J, VASWANI A.Self-attention with relative position representations[C]//Proceedings of 2018 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.Stroudsburg, USA:Association for Computational Linguistics, 2018:1-25. [13] MIKOLOV T, CHEN K, CORRADO G, et al.Efficient estimation of word representations in vector space[EB/OL].[2021-03-16].https://arxiv.org/abs/1301.3781. [14] 张华伟.基于Word2Vec的神经网络协同推荐模型[J].网络空间安全, 2019, 10(6):25-28. ZHANG H W.Neural network cooperative recommendation model based on Word2Vec[J].Cyberspace Security, 2019, 10(6):25-28.(in Chinese) [15] 章跃琳.基于Word2Vec的在线商品特征提取与文本分类研究[D].温州:温州大学, 2019. ZHANG Y L.Research on feature extraction and text classification of online commodity based on Word2Vec[D].Wenzhou:Wenzhou University, 2019.(in Chinese) [16] LEI S.Research on the improved Word2Vec optimization strategy based on statistical language model[C]//Proceedings of International Conference on Information Science, Parallel and Distributed Systems.Washington D.C., USA:IEEE Press, 2020:356-359. [17] YAN H, DENG B C, LI X N, et al.TENER:adapting Transformer encoder for named entity recognition[EB/OL].[2021-03-16].https://arxiv.org/abs/1911.04474. [18] 张应成, 杨洋, 蒋瑞, 等.基于BiLSTM-CRF的商情实体识别模型[J].计算机工程, 2019, 45(5):308-314. ZHANG Y C, YANG Y, JIANG R, et al.Commercial intelligence entity recognition model based on BiLSTM-CRF[J].Computer Engineering, 2019, 45(5):308-314.(in Chinese) [19] DAI N, LIANG J Z, QIU X P, et al.Style Transformer:unpaired text style transfer without disentangled latent representation[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.Stroudsburg, USA:Association for Computational Linguistics, 2019:5997-6007. [20] GAO M, XIAO Q F, WU S C, et al.An attention-based ID-CNNs-CRF model for named entity recognition on clinical electronic medical records[C]//Proceedings of International Conference on Artificial Neural Networks.Berlin, Germany:Springer, 2019:231-242. |