基于标签聚类的中文重叠命名实体识别方法

doi:10.19678/j.issn.1000-3428.0054094

摘要/Abstract

摘要： 为解决命名实体之间的复杂嵌套以及语料库中标注误差导致的相邻命名实体边界重叠问题，提出一种中文重叠命名实体识别方法。利用基于随机合并与拆分的层次化聚类算法将重叠命名实体标签划分到不同的聚类簇中，建立文字到实体标签之间的一对一关联关系，解决了实体标签聚类陷入局部最优的问题，并在每个标签聚类簇中采用融合中文部首的BiLSTM-CRF模型提高重叠命名实体的识别稳定性。实验结果表明，该方法通过标签聚类的方式有效避免标注误差对识别过程的干扰，F1值相比现有识别方法平均提高了0.05。

关键词: 命名实体识别, 实体重叠, 中文命名实体, 标签聚类, 层次化聚类

Abstract: To address complex nested relations between named entities and overlapping boundaries of adjacent named entities caused by mislabeling in corpus,this paper proposes a method of Chinese overlapping Named Entity Recognition(NER).First,a hierarchical clustering algorithm based on random merging and splitting is used to divide the labels of overlapping named entities into different clusters to build one-to-one relations between words and entity labels,which prevents the clustering of entity labels from falling into local optimization.Then,a Bidirectional Long Short Term Memory-Conditional Random Fields(BiLSTM-CRF) model integrating Chinese radicals is used in each label clustering to improve the stability of overlapping NER.Experimental results show that the proposed method can effectively avoid the impact of mislabeling on recognition through label clustering,improving the F1 value by 0.05 compared with the existing methods.

Key words: Named Entity Recognition(NER), entity overlapping, Chinese named entity, label clustering, hierarchical clustering

中图分类号:

TP391.1

温秀秀, 马超, 高原原, 康子路. 基于标签聚类的中文重叠命名实体识别方法[J]. 计算机工程, 2020, 46(5): 41-46.

WEN Xiuxiu, MA Chao, GAO Yuanyuan, KANG Zilu. Chinese Overlapping Named Entity Recognition Method Based on Label Clustering[J]. Computer Engineering, 2020, 46(5): 41-46.

https://www.ecice06.com/CN/Y2020/V46/I5/41

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参考文献

[1] LIU Liu,WANG Dongbo.A survey of named entity recognition[J].Journal of Intelligence,2018,37(3):329-340.(in Chinese)刘浏,王东波.命名实体识别研究综述[J].情报学报,2018,37(3):329-340.
[2] MINTZ M,BILLS S,SNOW R,et al.Distant supervision for relation extraction without labeled data[C]//Proceedings of Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP.Palo Alto,USA:Association for Computational Linguistics,2009:1003-1011.
[3] CHANG K W,SAMDANI R,ROTH D.A constrained latent variable model for coreference resolution[C]//Proceedings of 2013 Conference on Empirical Methods in Natural Language Processing.Palo Alto,USA:Association for Computational Linguistics,2013:601-612.
[4] LIU Yan,FU Zhijie,LI Jiao,et al.Generation of medical encyclopedia knowledge graph[J].Chinese Journal of Medical Library and Information Science,2018,27(6):28-34.(in Chinese)刘燕,傅智杰,李姣,等.医学百科知识图谱构建[J].中华医学图书情报杂志,2018,27(6):28-34.
[5] WANG Hongbin,SHEN Qiang,XIAN Yantuan.Research on Chinese named entity recognition fusing transfer learning[J].Journal of Chinese Computer Systems,2017,38(2):346-351.(in Chinese)王红斌,沈强,线岩团.融合迁移学习的中文命名实体识别[J].小型微型计算机系统,2017,38(2):346-351.
[6] YANG Jinfeng,GUAN Yi,HE Bin,et al.Corpus construction for named entities and entity relations on Chinese electronic medical records[J].Journal of Software,2016,27(11):2725-2746.(in Chinese)杨锦锋,关毅,何彬,等.中文电子病历命名实体和实体关系语料库构建[J].软件学报,2016,27(11):2725-2746.
[7] YANG Jinfeng,YU Qiubin,GUAN Yi,et al.An overview of research on electronic medical record oriented named entity recognition and entity relation extraction[J].Acta Automatica Sinica,2014,40(8):1537-1562.(in Chinese)杨锦锋,于秋滨,关毅,等.电子病历命名实体识别和实体关系抽取研究综述[J].自动化学报,2014,40(8):1537-1562.
[8] KATIYAR A,CARDIE C.Nested named entity recognition revisited[C]//Proceedings of NAACL-HLT'18.New Orleans,USA:NAACL Press,2018:861-871.
[9] DONG Chuanhai,ZHANG Jiajun,ZONG Chengqing,et al.Character-based LSTM-CRF with radical-level features for Chinese named entity recognition[C]//Proceedings of 2016 International Conference on Computer Processing of Oriental Languages Natural Language.Berlin,Germany:Springer,2016:239-250.
[10] ZHANG Chuanyan,HONG Xiaoguang,PENG Zhaohui,et al.Extracting Web entity activities based on SVM and extended conditional random fields[J].Journal of Software,2012,23(10):2612-2627.(in Chinese)张传岩,洪晓光,彭朝晖,等.基于SVM和扩展条件随机场的Web实体活动抽取[J].软件学报,2012,23(10):2612-2627.
[11] TENG Qingqing,JI Jiuming,ZHENG Rongting,et al.Applicability analysis of Chinese named entity recognition method based on literatures[J].Journal of Intelligence,2010,29(9):157-161,169.(in Chinese)滕青青,吉久明,郑荣廷,等.基于文献的中文命名实体识别算法适用性分析研究[J].情报杂志,2010,29(9):157-161,169.
[12] ZHANG Yuejie,XU Zhiting,XUE Xiangyang.Fusion of multiple features for Chinese named entity recognition based on maximum entropy model[J].Journal of Computer Research and Development,2008,45(6):1004-1010.(in Chinese)张玥杰,徐智婷,薛向阳.融合多特征的最大熵汉语命名实体识别模型[J].计算机研究与发展,2008,45(6):1004-1010.
[13] TAN Yongxin,LUO Junyong,YIN Meijuan.Markov logic network and its applications in information extraction[J].Computer Engineering,2012,38(18):162-165.(in Chinese)谭永兴,罗军勇,尹美娟.Markov逻辑网及其在信息抽取中的应用[J].计算机工程,2012,38(18):162-165.
[14] LAFFERTY J D,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.San Francisco,USA:Morgan Kaufmann Publishers,2001:282-289.
[15] MA X Z,HOVY E.End-to-end sequence labeling via Bi-directional LSTM-CNNsCRF[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.Palo Alto,USA:Association for Computational Linguistics,2016:1064-1074.
[16] LAMPLE G,BALLESTEROS M,SUBRAMANIAN S,et al.Neural architectures for named entity recognition[C]//Proceedings of 2016 NAACL-HLT'16.New Orleans,USA:NAACL Press,2016:260-270.
[17] ZHANG Yue,YANG Jie.Chinese NER using lattice LSTM[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics.Palo Alto,USA:Association for Computational Linguistics,2018:1554-1564.
[18] JU M,MIWA M,ANANIADOU S.A neural layered model for nested named entity recognition[C]//Proceedings of 2018 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.Palo Alto,USA:Association for Computational Linguistics,2018:1446-1459.
[19] BEATRICE A,BARRY H,CLAIRE G.Recognizing nested named entities in biomedical text[C]//Proceedings of BioNLP'07.Palo Alto,USA:Association for Computational Linguistics,2007:65-72.
[20] NGUYEN T S,NGUYEN L M.Nested named entity recognition using multilayer recurrent neural networks[C]//Proceedings of 2017 International Conference of the Pacific Association for Computational Linguistics.Palo Alto,USA:Association for Computational Linguistics,2017:233-246.
[21] SOHRAB M G,MIWA M.Deep exhaustive model for nested named entity recognition[C]//Proceedings of 2018 Conference on Empirical Methods in Natural Language Processing.Palo Alto,USA:Association for Computational Linguistics,2018:2843-2849.

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