Abstract
Natural language processing is an important research direction and research hotspot in the field of artificial intelligence. Named entity recognition is one of the key tasks, which is to identify entities with specific meanings in the text, such as names of people, places, institutions, proper nouns, etc. Traditional named entity recognition methods are mainly implemented based on rules, dictionaries, and statistical learning. In recent years, with the rapid expansion of Internet text data scale and the rapid development of deep learning technology, a large number of deep neural network-based methods have emerged, which have greatly improved the accuracy of recognition. This paper attempts to summarize the traditional methods and the latest research progress in the field of named entity identification, and summarize and analyse its main models, algorithms and applications. Finally, the future development trend of named entity recognition is discussed.
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Chinchor N (1995) MUC-6 named entity task definition (version 2.1). In: Proceedings of the 6th conference on message understanding, Columbia, Maryland; Bakushinsky A, Goncharsky A (1994) Ill-posed problems theory and applications
Chinchor N, Robinson P (1997) MUC-7 named entity task definition. In: Proceedings of the 7th conference on message understanding, Columbia, Maryland
Sun Z, Wang H (2010) Overview on the advance of the research on name entity recognition. Data Anal Knowl Disc 26(6):42–47 (中文)
Rau LF (1991) Extracting company names from text. In: Proceedings of the seventh IEEE conference on artificial intelligence applications. IEEE
Zhang X, Wang L (1997) Identification and analysis of chinese organization and institution names. J Chin Inf Process 11(4):22–33 (中文)
Farmakiotou D, Karkaletsis V, Koutsias J et al (2000) Rule-based named entity recognition for Greek financial texts. In: Proceedings of the workshop on computational lexicography and multimedia dictionaries (COMLEX 2000), pp 75–78
Wang N, Ge R, Yuan C et al (2002) Company name identification in Chinese financial domain. Chin J Inf Sci 16(2):1–6 (中文)
Li H (2012) Statistical learning method. Tsinghua University Press (中文)
Bikel DM, Miller S, Schwartz R et al (1998) Nymble: a high-performance learning name-finder. arXiv preprint cmp-lg/9803003
Bikel DM, Schwartz R, Weischedel RM (1999) An algorithm that learns what’s in a name. Mach Learn 34(1–3):211–231
Liu J (2009) Chinese named entity recognition algorithm based on improved hidden Markov model. J Taiyuan Normal Univ Nat Sci Ed 1:80–83 (中文)
Krishnan V, Manning CD (2006) Association for computational linguistics the 21st international conference, Sydney, Australia (2006.07.17–2006.07.18). Proceedings of the 21st international conference on computational linguistics and the 44th annual meeting of the ACL, ACL’06—An effective two-stage model for exploiting non-local dependencies in named entity recognition. International conference on computational linguistics & the meeting of the association for computational linguistics. Association for Computational Linguistics, pp 1121–1128
Borthwick A, Sterling J, Agichtein E et al (1998) NYU: description of the MENE named entity system as used in MUC-7. In: Seventh message understanding conference (MUC-7): proceedings of a conference held in Fairfax, Virginia, April 29–May 1998
Sekine S, Grishman R, Shinnou H (1998) A decision tree method for finding and classifying names in Japanese texts. In: Sixth workshop on very large corpora
Takeuchi K, Collier N (2002) Use of support vector machines in extended named entity recognition, In: Proceedings of the 6th conference on natural language learning, vol 20. Association for Computational Linguistics, pp 1–7
De Meulder F, Daelemans W (2003) Memory-based named entity recognition using unannotated data. In: Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003, vol 4. Association for Computational Linguistics, pp 208–211
Zhou J, Dai X, Yin C et al (2006) Automatic recognition of Chinese organization name based on cascaded conditional random fields. Chin J Electron 34(5):804–809 (中文)
Yu H, Zhang H, Liu Q et al (2006) Automatic recognition of Chinese organization name based on cascaded conditional random fields. Trans Commun 2 (中文)
Liao W, Veeramachaneni S (2009) A simple semi-supervised algorithm for named entity recognition. In: Proceedings of the NAACL HLT 2009 workshop on semi-supervised learning for natural language processing. Association for Computational Linguistics, pp 58–65
Collobert R, Weston J, Bottou L et al (2011) Natural language processing (almost) from scratch. J Mach Learn Res 12(Aug):2493–2537
Huang Z, Xu W, Yu K (2015) Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991
Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473
Rei M, Crichton GKO, Pyysalo S (2016) Attending to characters in neural sequence labeling models. arXiv preprint arXiv:1611.04361
Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359
Zirikly A, Hagiwara M (2015) Cross-lingual transfer of named entity recognizers without parallel corpora. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing, vol 2: Short papers, pp 390–396
Wang M, Manning CD (2014) Cross-lingual projected expectation regularization for weakly supervised learning. Trans Assoc Comput Linguist 2:55–66
Kim JD, Ohta T, Tateisi Y et al (2003) GENIA corpus—a semantically annotated corpus for bio-textmining. Bioinformatics 19(suppl_1):i180–i182
Ritter A, Clark S, Etzioni O (2011) Named entity recognition in tweets: an experimental study. In: Proceedings of the conference on empirical methods in natural language processing. Association for Computational Linguistics, pp 1524–1534
Yang Z, Salakhutdinov R, Cohen WW (2017) Transfer learning for sequence tagging with hierarchical recurrent networks. arXiv preprint arXiv:1703.06345
Peters ME, Ammar W, Bhagavatula C et al (2017) Semi-supervised sequence tagging with bidirectional language models. arXiv preprint arXiv:1705.00108
Zheng Q, Liu Q, Wang Z et al (2010) Research and development on biomedical named entity recognition. J Comput Appl 27(3) (中文)
Zhang F, Wang M (2017) Medical text entities recognition method base on deep learning. Comput Technol Autom 36(1):123 (中文)
Acknowledgements
This work was supported in part by National Natural Science Foundation of China (No. 61701284, No. 61702306, No. 61602278), Ministry of Education Humanities and Social Sciences Research Youth Fund Project (17YJCZH187) and Qingdao Philosophy, Social Science Planning Project (QDSKL1801131).
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Wen, Y., Fan, C., Chen, G., Chen, X., Chen, M. (2020). A Survey on Named Entity Recognition. In: Liang, Q., Wang, W., Liu, X., Na, Z., Jia, M., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2019. Lecture Notes in Electrical Engineering, vol 571. Springer, Singapore. https://doi.org/10.1007/978-981-13-9409-6_218
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