计算机科学 ›› 2024, Vol. 51 ›› Issue (3): 198-204.doi: 10.11896/jsjkx.230200114
廖梦1, 贾真1, 李天瑞1,2,3
LIAO Meng1, JIA Zhen1, LI Tianrui1,2,3
摘要: 随着中文命名实体识别研究的不断深入,大多数模型关注融入词汇或字形信息来丰富特征表示,但是却忽略了标签信息。因此文中提出了一种融合标签信息的中文命名实体识别模型。首先,通过预训练模型BERT-wwm得到字符的嵌入表示,并将标签向量化,使用Transformer解码器结构将字符表示与标签表示进行交互学习,捕捉字符与标签的相互依赖关系,丰富字符的特征表示。为了促进标签信息的学习,构建了基于文本句的监督信号,增加了多标签文本分类任务,采用多任务学习的方式进行训练。其中,命名实体识别任务采用条件随机场进行解码预测,多标签文本分类任务采用双仿射机制进行解码预测,两任务共享除解码层以外的所有参数,保证了不同的监督信息反馈到每个子任务。在公开数据集MSRA,Weibo和Resume上进行了多组对比实验,分别获得了95.75%,72.17%,96.23%的F1值。与多个基准模型相比,所提模型的实验效果有一定的提升,证明了该模型的有效性与可行性。
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[1]LI J Q,CHEN X J,WANG D K,et al.Enhancing Label Representations with Relational Inductive Bias Constraint for Fine-Grained Entity Typing[C]//International Joint Conferences on Artificial Intelligence.2021:3843-3849. [2]LIN Y,JI H.An attentive fine-grained entity typing model with latent type representation[C]//Proceedings of the 2019 Confe-rence on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing(EMNLP-IJCNLP).2019:6197-6202. [3]LI J Q,ZHAO S H,YANG J J,et al.WCP-RNN:a novel RNN-based approach for Bio-NER in Chinese EMRs[J].The journal of supercomputing,2020,76(3):1450-1467. [4]JIA Y Z,MA X P.Attention in character-Based BiLSTM-CRF for Chinese named entity recognition[C]//Proceedings of the 2019 4th International Conference on Mathematics and Artificial Intelligence.2019:1-4. [5]PENG D L,WANG Y R,LIU C,et al.TL-NER:A transferlearning model for Chinese named entity recognition[J].Information Systems Frontiers,2020,22(6):1291-1304. [6]VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[C]//Advances in Neural Information Processing Systems.2017:5998-6008. [7]WANG C Q,CHEN W,XU B.Named entity recognition withgated convolutional neural networks[C]//Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data.2017:110-121. [8]YAN H,DENG B C,LI X N,et al.TENER:adapting transfor-mer encoder for named entity recognition[J].arXiv:1911.04474,2019. [9]JIN Y L,XIE J F,GUO W S,et al.LSTM-CRF neural network with gated self attention for Chinese NER[J].IEEE Access,2019,7:136694-136703. [10]CHANG Y,KONG L,JIA K J,et al.Chinese named entity recognition method based on BERT[C]//2021 IEEE International Conference on Data Science and Computer Application(ICDSCA).2021:294-299. [11]DONG C H,ZHANG J J,ZONG C Q,et al.Character-based LSTM-CRF with radical-level features for Chinese named entity recognition[C]//5th CCF Conference on Natural Language Processing and Chinese Computing.2016:239-250. [12]LIU Y H,LIU C J,XU R F,et al.Utilizing glyph feature and ite-rative learning for named entity recognition in finance text[J].Journal of Chinese Information Processing,2020,34(11):74-83. [13]ZHANG D,WANG M T,CHEN W L.Named entity recognition combining wubi glyphs with contextualized character embeddings[J].Computer Engineering,2021,47(3):94-101. [14]MENG Y X,WU W,WANG F,et al.Glyce:Glyph-vectors forchinese character representations[J].Advances in Neural Information Processing Systems,2019,32:2746-2757. [15]SONG C H,SEHANOBISH A.Using chinese glyphs for named entity recognition[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2020:13921-13922. [16]XUAN Z Y,BAO R,JIANG S Y.FGN:Fusion glyph networkfor Chinese named entity recognition[C]//China Conference on Knowledge Graph and Semantic Computing.2020:28-40. [17]ZHANG Y,YANG J.Chinese NER Using Lattice LSTM[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics(Volume 1:Long Papers).2018:1554-1564. [18]GUI T,ZOU Y C,ZHANG Q,et al.A lexicon-based graph neural network for chinese ner[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing(EMNLP-IJCNLP).2019:1039-1049. [19]SUI D B,CHEN Y B,LIU K,et al.Leverage lexical knowledge for chinese named entity recognition via collaborative graph network[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing(EMNLP-IJCNLP).2019:3821-3831. [20]LI X N,YAN H,QIU X P,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. [21]LIU W,XU T G,XU Q H,et al.An Encoding Strategy Based Word-Character LSTM for Chinese NER[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies,Volume 1(Long and Short Papers).2019:2379-2389. [22]MA R T,PENG M L,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.2020:5951-5960. [23]LIU W,FU X Y,ZHANG Y,et al.Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing(Volume 1:Long Papers).2021:5847-5858. [24]LI X Y,FENG J R,MENG Y X,et al.A Unified MRC Framework for Named Entity Recognition[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.2020:5849-5859. [25]YAN H,GUI T,DAI J Q,et al.A Unified Generative Framework for Various NER Subtasks[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing(Volume 1:Long Papers).2021:5808-5822. [26]JIMENEZ G B,MCNEAL N,WASHINGTON C,et al.Thin-king about GPT-3 In-Context Learning for Biomedical IE? Think Again[C]//Findings of the Association for Computa-tional Linguistics:EMNLP 2022.2022:4497-4512. [27]LI J Y,FEI H,LIU J,et al.Unified named entity recognition as word-word relation classification[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2022:10965-10973. [28]CUI Y M,CHE W X,LIU T,et al.Pre-training with whole word masking for chinese bert[J].IEEE/ACM Transactions on Au-dio,Speech,and Language Processing,2021,29:3504-3514. [29]CUI L Y,ZHANG Y.Hierarchically-Refined Label AttentionNetwork for Sequence Labeling[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Proces-sing and the 9th International Joint Conference on Natural Language Processing(EMNLP-IJCNLP).2019:4115-4128. [30]DONG Y,CORDONNIER J B,LOUKAS A.Attention is not all you need:Pure attention loses rank doubly exponentially with depth[C]//International Conference on Machine Learning.2021:2793-2803. [31]LEVOW G A.The third international Chinese language processing bakeoff:Word segmentation and named entity recognition[C]//Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing.2006:108-117. [32]PENG N,DREDZE M.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. [33]ZHU Y Y,WANG G X.CAN-NER:Convolutional AttentionNetwork for Chinese Named Entity Recognition[C]//Procee-dings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies,Volume 1(Long and Short Papers).2019:3384-3393. |
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