Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 1-10.doi: 10.11896/jsjkx.201100165

• Intelligent Computing • Previous Articles     Next Articles

Overview of Nested Named Entity Recognition

YU Shi-yuan1,2, GUO Shu-ming2, HUANG Rui-yang2, ZHANG Jian-peng2, SU Ke1,2   

  1. 1 School of Software,Zhengzhou University,Zhengzhou 450001,China
    2 National Digital Switching System Engineering and Technological R&D Center,Zhengzhou 450002,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:YU Shi-yuan,born in 1996,postgraduate.Her main research interests include natural language processing and know-ledge graph.
    GUO Shu-ming,born in 1977,Ph.D,master supervisor,associate researcher.His main research interests include data science and radio communication and big data technology.
  • Supported by:
    Youth Program of National Natural Science Foundation of China(620023840).

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Abstract: There are rich semantic relations and structural information between nested named entities,which is very important for the implementation of downstream tasks such as relation extraction and event extraction.The accuracy of text information extraction model has gradually exceeded the traditional rule-based method.Therefore,many scholars have carried out research on nested named entity recognition technology based on deep learning,and obtained the most advanced performance.This paper reviews the existing nested named entity recognition technology,This paper gives a comprehensive review of the existing nested named entity recognition technology,introduces the most representative methods and the latest application technology of nested named entity recognition,and discusses and prospects the challenges and development direction in the future.

Key words: Hypergraph, Named entity recognition, Nested named entities, Sequence labeling, Span

CLC Number: 

  • TP312
[1]DONOHO D L.Compressed sensing[J].IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[2]MITCHELL A,STRASSEL S,HUANG S D,et al.ACE 2004 Multilingual Training Corpuse[EB/OL].https://catalog.ldc.upenn.edu/LDC2005T09.
[3]WALKER C,STRASSEL S,MEDERO J,et al.ACE 2005 Multilingual Training Corpus[EB/OL].https://catalog.ldc.upenn.edu/LDC2006T06.
[4]ZHANG J,SHEN D,ZHOU G,et al.Enhancing HMM-based biomedical named entity recognition by studying special phenomena[J].Journal of Biomedical Informatics,2004,37(6):411-422.
[5]ZHOU G D.Recognizing names in biomedical texts using mutual information independence model and SVM plus sigmoid[J].International Journal of Medical Informatics,2006,75(6):456-467.
[6]ZHOU G D,ZHANG J,SU J,et al.Recognizing Names in Biomedical Texts:a Machine Learning Approach[J].Bioinfor-matics,2004,20(7):1178-1190.
[7]KIM J D,OHTA T,TATEISI Y,et al.GENIA corpus a semantically annotated corpus for bio-text mining[J].Bioinformatics,2003,19:180-182.
[8]ALEX B,HADDOW B,GROVER C.Recognizing Nested Named Entities in Biomedical Text[C]//Biological,Translational,And Clinical Language Processing.2017:65-72.
[9]FINKEL J R,MANNING C D.Nested Named Entity Recognition[C]//Empirical Methods in Natural Language Processing.2009:141-150.
[10]MUIS A O,LU W.Labeling Gaps Between Words:Recognizing Overlapping Mentions With Mention Separators[C]//Procee-dings of the 2017 Conference on Empirical Methods in Natural Language Processing.2017:2608-2618.
[11]WANG B,LU W,Neural Segmental Hypergraphs for Overlapping Mention Recognition[C]//Proceedings of the 2018 Confe-rence on Empirical Methods in Natural Language Processing.2018:204-214.
[12]KATIYAR A,CARDIE C.Nested Named Entity RecognitionRevisited[C]//North American Chapter of the Association for Computational Linguistics.2018:861-871.
[13]JU M,MIWA M,ANANIADOU S,et al.A Neural LayeredModel for Nested Named Entity Recognition[C]//North Ameri-can Chapter of the Association For Computational Linguistics.2018:1446-1459.
[14]STRAKOVA J,STRAKA M,HAJIC J,et al.Neural Architectures for Nested NER Through Linearization[C]//Meeting of the Association for Computational Linguistics.2019:5326-5331.
[15]TAKASHI S,EDUARD H.Nested Named Entity Recognition via Second-best Sequence Learning and Decoding[J].Transactions of the Association for Computational Linguistics,2020,8:605-620.
[16]XU M,JIANG H,WATCHARA W S,et al.A Local Detection Approach for Named Entity Recognition and Mention Detection[C]//Meeting of the Association for Computational Linguistics.2017:1237-1247.
[17]ZHANG S L,JIANG H,XU M B,et al.The Fixed-Size Ordinally-Forgetting Encoding Method for Neural Network Language Models[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2:Short Papers).2015:495-500.
[18]SOHRAB M G,MIWA M.Deep Exhaustive Model for Nested Named Entity Recognition[C]//Empirical Methods In Natural Language Processing.2018:2843-2849.
[19]FISHER J,VLACHOS A.Merge And Label:A Novel Neural Network Architecture For Nested NER[C]//Meeting of the Association for Computational Linguistics.2019:5840-5850.
[20]XIA C Y,ZHANG C W,YANG T,et al.Multi-Grained Named Entity Recognition[C]//Proceedings of the 57th Annual Mee-ting of the Association for Computational Linguistics.2019:1430-1440.
[21]LUAN Y,WADDEN D,HE L,et al.A General Framework for Information Extraction Using Dynamic Span Graphs[C]//North American Chapter of the Association for Computational Linguistics.2019:3036-3046.
[22]ZHENG C,CAI Y,XU J,et al.A Boundary-Aware NeuralModel for Nested Named Entity Recognition[C]//International Joint Conference on Natural Language Processing.2019:357-366.
[23]LIN H,LU Y,HAN X,et al.Sequence-To-Nuggets:Nested Entity Mention Detection via Anchor-Region Networks[C]//Meeting of the Association for Computational Linguistics.2019:5182-5192.
[24]TAN C,QIU W,CHEN M,et al.Boundary Enhanced NeuralSpan Classification for Nested Named Entity Recognition[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2020:9016-9023.
[25]WANG B,LU W,WANG Y,et al.A Neural Transition-Based Model for Nested Mention Recognition[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.2018:1011-1017.
[26]LI X,FENG J,MENG Y,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.
[27]LUO Y,ZHAO H.Bipartite Flat-Graph Network for NestedNamed Entity Recognition[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.2020:6408-6418.
[28]LUAN Y,HE L,OSTENDORF M,et al.Multi-Task Identification of Entities,Relations,and Coreference for Scientific Know-ledge Graph Construction [C]//Empirical Methods in Natural Language Processing.2018:3219-3232.
[29]RINGLAND N,DAI X,KARIMI S,et al.NNE:A Dataset For Nested Named Entity Recognition In English Newswire[C]//Meeting of the Association for Computational Linguistics.2019:5176-5181.
[30]KARIMI S,METKEJIMENEZ A,KEMP M,et al.Cadec:ACorpus of Adverse Drug Event Annotations[J].Journal of Biomedical Informatics,2015,55:73-81.
[31]ZHENG S,WANG F,BAO H,et al.Joint Extraction of Entities And Relations Based On A Novel Tagging Scheme [C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics.2017:1227-1236.
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