Systematic Literature Review on Named Entity Recognition: Approach, Method, and Application
Keywords:
Named entity recognition, entity extraction, entity detection, entity classification, natural language processing
Abstract
Named entity recognition (NER) is one of the preprocessing stages in natural language processing (NLP), which functions to detect and classify entities in the corpus. NER results are used in various NLP applications, including sentiment analysis, text summarization, chatbot, machine translation, and question answering. Several previous reviews partially discussed NER, for instance, NER reviews in specific domains, NER classification, and NER deep learning. This paper provides a comprehensive and systematic review on NER topic studies published from 2011 to 2020. The main contribution of this review is to present a comprehensive systematic literature review on NER from preprocessing techniques, datasets, application domains, feature extraction techniques, approaches, methods, and evaluation techniques. The result concludes that the deep learning approach and the Bi-directional long short-term memory with a conditional random field (Bi-LSTM-CRF) method are the most interesting methods among NER researchers. At the same time, medical and health are NER researchers' most popular domains. These developments have also led to an increasing number of public datasets in the medical and health fields. At the end of this review, we recommend some opportunities and challenges for NER research going forward.References
1. C. C. Aggarwal and C. Zhai, An introduction to text mining, in Mining Text Data, eds. C. C.
Aggarwal and C. Zhai (Springer US, Boston, MA, 2012), pp. 1–10.
2. J. Jiang, Information extraction from text, in Mining Text Data, eds. C. C. Aggarwal and C. Zhai
(Springer US, Boston, MA, 2012), pp. 11–41.
3. J. Piskorski and R. Yangarber, Information extraction: past, present and future, in Multi-source,
Multilingual Information Extraction and Summarization, eds. T. Poibeau, H. Saggion, J. Piskorski and R. Yangarber (Springer, Berlin, Heidelberg, 2013), pp. 23–49.
4. I. Augenstein, L. Derczynski and K. Bontcheva, Generalisation in named entity recognition: a
quantitative analysis, Comput. Speech Lang. 44 (2017) 61–83.
5. A. Goyal, V. Gupta and M. Kumar, Analysis of different supervised techniques for named entity
recognition, in Adv. Inform. Comput. Res., eds. A. K. Luhach, D. S. Jat, K. B. G. Hawari, X.-Z.
Gao and P. Lingras (Springer, Singapore, 2019), pp. 184–195.
6. Appendix C: Named entity task definition (v2.1), in Sixth Message Underst. Conf. MUC-6 Proc.
Conf. Held Columbia Md. Novemb. 6-8 1995 (Columbia, 1995).
7. M. Marrero, J. Urbano, S. Sánchez-Cuadrado, J. Morato and J. M. Gómez-Berbís, Named entity
recognition: fallacies, challenges and opportunities, Comput. Stand. Interfaces 35(5) (2013)
482–489.
8. M. S. Salleh, S. A. Asmai, H. Basiron and S. Ahmad, A Malay named entity recognition using
conditional random fields, in 2017 5th Int. Conf. Inf. Commun. Technol. ICoIC7 (IEEE, Melaka,
Malaysia, 2017), pp. 1–6.
9. L. Derczynski, D. Maynard, G. Rizzo, M. van Erp, G. Gorrell, R. Troncy, J. Petrak and K.
Bontcheva, Analysis of named entity recognition and linking for tweets, Inf. Process. Manag.
51(2) (2015) 32–49.
10. S. Eltyeb and N. Salim, Chemical named entities recognition: a review on approaches and applications, J. Cheminformatics 6 (2014) 17.
11. D. Nadeau, P. D. Turney and S. Matwin, Unsupervised named-entity recognition: generating
gazetteers and resolving ambiguity, in Advances in Artificial Intelligence, eds. A. Y. Tawfik and
S. D. Goodwin (Springer Berlin Heidelberg, Berlin, Heidelberg, 2006), pp. 266–277.
12. S. J. Pan, Z. Toh and J. Su, Transfer joint embedding for cross-domain named entity recognition,
ACM Trans. Inf. Syst. 31(2) (2013) 1–27.
13. L. Li, W. Fan and D. Huang, A two-phase Bio-NER system based on integrated classifiers and
multiagent strategy, IEEE/ACM Trans. Comput. Biol. Bioinform. 10(4) (2013) 897–904.
14. A. Akkasi and E. Varoglu, Improving biochemical named entity recognition using pso classifier
selection and bayesian combination methods, IEEE/ACM Trans. Comput. Biol. Bioinform. 14(6)
(2017) 1327–1338.
15. Y. Wen, C. Fan, G. Chen, X. Chen and M. Chen, A survey on named entity recognition, in
Communications, Signal Processing, and Systems, eds. Q. Liang, W. Wang, X. Liu, Z. Na, M. Jia
and B. Zhang (Springer Singapore, Singapore, 2020), pp. 1803–1810.
16. Y. Chen, T. A. Lasko, Q. Mei, J. C. Denny and H. Xu, A study of active learning methods for
named entity recognition in clinical text, J. Biomed. Inform. 58 (2015) 11–18.
17. J. A. Lossio-Ventura, C. Jonquet, M. Roche and M. Teisseire, Biomedical term extraction: overview
and a new methodology, Inf. Retr. J. 19(1-2) (2016) 59–99.
18. U. Kanimozhi and D. Manjula, A systematic review on biomedical named entity recognition,
in Data Science Analytics and Applications, eds. S. R and M. Sharma (Springer Singapore,
Singapore, 2018), pp. 19–37.
19. G. Popovski, B. K. Seljak and T. Eftimov, A survey of named-entity recognition methods for food
information extraction, IEEE Access 8 (2020) 31586–31594.
20. D. Nadeau and S. Sekine, A survey of named entity recognition and classification, Lingvisticae
Investig. 30(1) (2007) 3–26
21. A. Goyal, V. Gupta and M. Kumar, Recent named entity recognition and classification techniques:
a systematic review, Comput. Sci. Rev. 29 (2018) 21–43.
22. V. T. Chakaravarthy, V. Pandit, S. Roy, P. Awasthi and M. K. Mohania, Decision trees for entity identification: approximation algorithms and hardness results, ACM Trans. Algorithms 7(2)
(2011) 1–22.
23. X. Liao and Z. Zhao, Unsupervised approaches for textual semantic annotation, a survey, ACM
Comput. Surv. 52(4) (2019) 1–45.
24. A. Thomas and S. Sangeetha, Deep learning architectures for named entity recognition: a survey,
in Adv. Comput. Intell. Eng., eds. B. Pati, C. R. Panigrahi, R. Buyya and K.-C. Li (Springer,
Singapore, 2020), pp. 215–225.
25. J. Li, A. Sun, J. Han and C. Li, A survey on deep learning for named entity recognition, IEEE
Trans. Knowl. Data Eng. (2020) 1–1.
26. A. Dandashi, J. Al Jaam and S. Foufou, Arabic named entity recognition—a survey and analysis,
in Intelligent Interactive Multimedia Systems and Services 2016, Series in Smart Innovation, Systems and Technologies, Vol.55, eds. G. Pietro, L. Gallo, R. Howlett and L. Jain (Springer, Cham,
2016), pp. 83–96.
27. K. Shaalan, A survey of Arabic named entity recognition and classification, Comput. Linguist.
40(2) (2014) 469–510.
28. B. A. Kitchenham, Systematic review in software engineering: where we are and where we should
be going, in Proc. 2nd Int. Workshop Evidential Assess. Softw. Technol. - EAST 12 (ACM Press,
Lund, Sweden, 2012), p. 1.
29. C. Okoli and K. Schabram, A guide to conducting a systematic literature review of information
systems research, SSRN Electron. J. (2010).
30. A. Ekbal and S. Saha, Stacked ensemble coupled with feature selection for biomedical entity
extraction, Knowl.-Based Syst. 46(2013) 22–32.
31. A. Ekbal and S. Saha, Simultaneous feature and parameter selection using multiobjective optimization: application to named entity recognition, Int. J. Mach. Learn. Cybern. 7(4) (2016)
597–611.
32. A. Ekbal and S. Saha, Combining feature selection and classifier ensemble using a multiobjective simulated annealing approach: application to named entity recognition, Soft Comput. 17(1)
(2013) 1–16.
33. A. Ekbal, S. Saha and U. K. Sikdar, Biomedical named entity extraction: some issues of corpus
compatibilities, SpringerPlus 2(1) (2013) 601.
34. A. Ekbal and S. Saha, Weighted vote-based classifier ensemble for named entity recognition: a
genetic algorithm-based approach, ACM Trans. Asian Lang. Inf. Process. 10(2) (2011) 1–37.
35. A. Ekbal and S. Saha, A multiobjective simulated annealing approach for classifier ensemble:
named entity recognition in indian languages as case studies, Expert Syst. Appl. 38(12) (2011)
14760–14772.
36. S. Saha and A. Ekbal, Combining multiple classifiers using vote based classifier ensemble technique for named entity recognition, Data Knowl. Eng. 85 (2013) 15–39.
37. S. Yadav, A. Ekbal and S. Saha, Feature selection for entity extraction from multiple biomedical
corpora: A PSO-based approach, Soft Comput. 22(20) (2018) 6881–6904.
38. S. Yadav, A. Ekbal and S. Saha, Information theoretic-PSO-based feature selection: an application
in biomedical entity extraction, Knowl. Inf. Syst. 60(3) (2019) 1453–1478.
39. L. Sun, F. Ji, K. Zhang and C. Wang, Multilayer ToI detection approach for nested NER, IEEE
Access 7 (2019) 186600–186608.
40. L. Sun, K. Zhang, Y. Sun, F. Weng and J. Zhang, ETIP: a lengthy nested NER problem for Chinese
insurance policy analysis, Pattern Anal. Appl. 23(4) (2020) 1755–1765.
41. L. Sun, Y. Sun, F. Ji and C. Wang, Joint learning of token context and span feature for span-based
nested NER, IEEEACM Trans. Audio Speech Lang. Process. 28 (2020) 2720–2730.
42. A. Akkasi, Sentence-based undersampling for named entity recognition using genetic algorithm,
Iran J. Comput. Sci. 1(3) (2018) 165–174.
43. H. Cho and H. Lee, Biomedical named entity recognition using deep neural networks with contextual information, BMC Bioinformatics 20(1) (2019) 735.
44. H. Cho, W. Choi and H. Lee, A method for named entity normalization in biomedical articles:
application to diseases and plants, BMC Bioinformatics 18(1) (2017) 451.
45. Y. Gao, L. Gu, Y. Wang, Y. Wang and F. Yang, Constructing a Chinese electronic medical record
corpus for named entity recognition on resident admit notes, BMC Med. Inform. Decis. Mak.
19(S2) (2019) 56.
46. Y. Gao, Y. Wang, P. Wang and L. Gu, Medical named entity extraction from Chinese resident
admit notes Using Character and Word Attention-Enhanced Neural Network, Int. J. Environ.
Res. Public. Health 17(5) (2020) 1614.
47. Q. Qiu, Z. Xie, L. Wu and L. Tao, GNER: a generative model for geological named entity recognition without labeled data using deep learning, Earth Space Sci. 6(6) (2019) 931–946.
48. Q. Qiu, Z. Xie, L. Wu, L. Tao and W. Li, BiLSTM-CRF for geological named entity recognition
from the geoscience literature, Earth Sci. Inform. 12(4) (2019) 565–579.
49. Z. Tang, L. Jiang, L. Yang, K. Li and K. Li, CRFs based parallel biomedical named entity recognition algorithm employing MapReduce framework, Clust. Comput. 18(2) (2015) 493–505.
50. Z. Tang, B. Wan and L. Yang, Word-character graph convolution network for Chinese named
entity recognition, IEEEACM Trans. Audio Speech Lang. Process. 28 (2020) 1520–1532.
51. N. Zhang, F. Li, G. Xu, W. Zhang and H. Yu, Chinese NER using dynamic meta-embeddings,
IEEE Access 7 (2019) 64450–64459.
52. N. Zhang, G. Xu, Z. Zhang and F. Li, MIFM: multi-granularity information fusion model for
Chinese named entity recognition, IEEE Access 7 (2019) 181648–181655.
53. G. Lample, M. Ballesteros, S. Subramanian, K. Kawakami and C. Dyer, Neural architectures for
named entity recognition, in Proc. 2016 Conf. North Am. Chapter Assoc. Comput. Linguist.
Hum. Lang. Technol. (Association for Computational Linguistics, San Diego, California, 2016),
pp. 260–270.
54. M. Ballesteros, C. Dyer and N. A. Smith, Improved transition-based parsing by modeling characters instead of words with LSTMs, ArXiv150800657 Cs (2015).
55. A. Ritter, S. Clark, Mausam and O. Etzioni, Named entity recognition in tweets: an experimental
study, in Proc. 2011 Conf. Empir. Methods Nat. Lang. Process. (Association for Computational
Linguistics, Edinburgh, Scotland, UK., 2011), pp. 1524–1534.
56. T. Eftimov, B. Koroušic Seljak and P. Korošec, A rule-based named-entity recognition method ´
for knowledge extraction of evidence-based dietary recommendations, PLOS ONE 12(6) (2017)
e0179488.
57. M. Dias, J. Boné, J. C. Ferreira, R. Ribeiro and R. Maia, Named entity recognition for sensitive
data discovery in Portuguese, Appl. Sci. 10(7) (2020) 2303.
58. W. Hemati and A. Mehler, LSTMVoter: chemical named entity recognition using a conglomerate
of sequence labeling tools, J. Cheminformatics 11(1) (2019) 3.
59. J. Atkinson and V. Bull, A multi-strategy approach to biological named entity recognition, Expert
Syst. Appl. 39(17) (2012) 12968–12974.
60. B. Bhasuran, G. Murugesan, S. Abdulkadhar and J. Natarajan, Stacked ensemble combined with
fuzzy matching for biomedical named entity recognition of diseases, J. Biomed. Inform. 64
(2016) 1–9.
61. C. Cabot, S. Darmoni and L. F. Soualmia, Cimind: a phonetic-based tool for multilingual named
entity recognition in biomedical texts, J. Biomed. Inform. 94 (2019) 103176.
62. L. Gligic, A. Kormilitzin, P. Goldberg and A. Nevado-Holgado, Named entity recognition in
electronic health records using transfer learning bootstrapped Neural Networks, Neural Netw.
121 (2020) 132–139.
63. Y. Zhang, O. Zhang, Y. Wu, H.-J. Lee, J. Xu, H. Xu and K. Roberts, Psychiatric symptom recognition without labeled data using distributional representations of phrases and on-line knowledge,
J. Biomed. Inform. 75 (2017) S129–S137.
64. M. Khalifa and K. Shaalan, Character convolutions for Arabic named entity recognition with long
short-term memory networks, Comput. Speech Lang. 58 (2019) 335–346.
65. M. Xiaofeng, W. Wei and X. Aiping, Incorporating token-level dictionary feature into neural
model for named entity recognition, Neurocomputing 375 (2020) 43–50.
66. C. C. Aggarwal, Machine Learning for Text (Springer International Publishing, New York, 2018).
67. M. Khabsa and C. L. Giles, Chemical entity extraction using CRF and an ensemble of extractors,
J. Cheminformatics 7(S1) (2015) S12.
68. A. Akkasi, E. Varoglu and N. Dimililer, ChemTok: a new rule based tokenizer for chemical named ˘
entity recognition, BioMed Res. Int. 2016 (2016) 1–9.
69. X. Cai, S. Dong and J. Hu, A deep learning model incorporating part of speech and self-matching
attention for named entity recognition of Chinese electronic medical records, BMC Med. Inform.
Decis. Mak. 19(S2) (2019) 65.
70. H.-C. Cho, N. Okazaki, M. Miwa and J. Tsujii, Named entity recognition with multiple segment
representations, Inf. Process. Manag. 49(4) (2013) 954–965.
71. V. Suárez-Paniagua, R. M. Rivera Zavala, I. Segura-Bedmar and P. Martínez, A two-stage deep
learning approach for extracting entities and relationships from medical texts, J. Biomed. Inform.
99 (2019) 103285.
72. B. Gaur, G. S. Saluja, H. B. Sivakumar and S. Singh, Semi-supervised deep learning based named
entity recognition model to parse education section of resumes, Neural Comput. Appl. 33(11)
(2020) 5705–5718.
73. C. Zhou, B. Li and X. Sun, Improving software bug-specific named entity recognition with deep
neural network, J. Syst. Softw. 165 (2020) 110572.
74. R. Batista-Navarro, R. Rak and S. Ananiadou, Optimising chemical named entity recognition
with pre-processing analytics, knowledge-rich features and heuristics, J. Cheminformatics 7(S1)
(2015) S6.
75. S. Bordag, C. Hänig and C. Beutenmüller, A structuralist approach for personal knowledge exploration systems on mobile devices, in Text Mining, eds. C. Biemann and A. Mehler (Springer
International Publishing, Cham, 2014), pp. 115–136.
76. H. Khalil, T. Osman and M. Miltan, Extracting Arabic composite names using genitive principles
of Arabic grammar, ACM Trans. Asian Low-Resour. Lang. Inf. Process. 19(4) (2020) 1–16.
77. Y.-Y. Hsu and H.-Y. Kao, Curatable named-entity recognition using semantic relations, IEEE/ACM
Trans. Comput. Biol. Bioinform. 12(4) (2015) 785–792.
78. L. Li, J. Zhao, L. Hou, Y. Zhai, J. Shi and F. Cui, An attention-based deep learning model for
clinical named entity recognition of Chinese electronic medical records, BMC Med. Inform.
Decis. Mak. 19(S5) (2019) 235.
79. G. Murugesan, S. Abdulkadhar, B. Bhasuran and J. Natarajan, BCC-NER: bidirectional, contextual clues named entity tagger for gene/protein mention recognition, EURASIP J. Bioinforma.
Syst. Biol. 2017(1) (2017) 7.
80. S. Song, N. Zhang and H. Huang, Named entity recognition based on conditional random fields,
Clust. Comput. 22(S3) (2019) 5195–5206.
81. R. Sivashankari and B. Valarmathi, NLP-MTFLR: document-level prioritization and identification of dominant multi-word named products in customer reviews, Arab. J. Sci. Eng. 43(2) (2018)
843–855.
82. Q. Zhao, D. Wang, J. Li and F. Akhtar, Exploiting the concept level feature for enhanced name
entity recognition in Chinese EMRs, J. Supercomput. 76(8) (2019) 6399–6420.
83. W. Huang, D. Hu, Z. Deng and J. Nie, Named entity recognition for Chinese judgment documents
based on BiLSTM and CRF, EURASIP J. Image Video Process. 2020(1) (2020) 52.
84. J. Wang, C. Lin, M. Li and C. Zaniolo, Boosting approximate dictionary-based entity extraction
with synonyms, Inf. Sci. 530 (2020) 1–21.
85. L. Li and Y. Jiang, Integrating language model and reading control gate in BLSTM-CRF for
biomedical named entity recognition, IEEE/ACM Trans. Comput. Biol. Bioinform. 17(3) (2018)
841–846.
86. Y. Wang, Y. Li, Z. Zhu, H. Tong and Y. Huang, Adversarial learning for multi-task sequence
labeling with attention mechanism, IEEEACM Trans. Audio Speech Lang. Process. 28 (2020)
1–1.
87. X. Zhong, E. Cambria and A. Hussain, Extracting time expressions and named entities with
constituent-based tagging schemes, Cogn. Comput. 12(4) (2020) 844–862.
88. X. Liu, N. Yang, Y. Jiang, L. Gu and X. Shi, A parallel computing-based deep attention model
for named entity recognition, J. Supercomput. 76(2) (2020) 814–830.
89. H.-S. Chang, S. Vembu, S. Mohan, R. Uppaal and A. McCallum, Using error decay prediction
to overcome practical issues of deep active learning for named entity recognition, Mach. Learn.
109(9-10) (2020) 1749–1778.
90. E. F. T. K. Sang and F. De Meulder, Introduction to the CoNLL-2003 shared task: languageindependent named entity recognition, ArXiv:Cs/0306050 (2003).
91. J. Wang, W. Xu, X. Fu, G. Xu and Y. Wu, ASTRAL: Adversarial trained LSTM-CNN for named
entity recognition, Knowl.-Based Syst. 197 (2020) 105842.
92. J. P. C. Chiu and E. Nichols, Named entity recognition with bidirectional LSTM-CNNs, Trans.
Assoc. Comput. Linguist. 4 (2016) 357–370.
93. G. Aguilar, F. AlGhamdi, V. Soto, M. Diab, J. Hirschberg and T. Solorio, Named entity recognition on code-switched data: overview of the calcs 2018 shared task, in Proc. Third Workshop
Comput. Approaches Linguist. Code-Switch. (Association for Computational Linguistics, Melbourne, Australia, 2018), pp. 138–147.
94. M. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee and L. Zettlemoyer, deep contextualized word representations, in Proc. 2018 Conf. North Am. Chapter Assoc. Comput. Linguist.
Hum. Lang. Technol. Vol. 1 Long Pap. (Association for Computational Linguistics, New Orleans,
Louisiana, 2018), pp. 2227–2237.
95. K. Clark, M.-T. Luong, C. D. Manning and Q. V. Le, Semi-supervised sequence modeling with
cross-view training, ArXiv Prepr. ArXiv180908370 (2018).
96. J. Devlin, M.-W. Chang, K. Lee and K. Toutanova, BERT: Pre-training of deep bidirectional
transformers for language understanding, ArXiv Prepr. ArXiv181004805 (2019).
97. Akbik, D. Blythe and R. Vollgraf, Contextual string embeddings for sequence labeling, in Proc.
27th Int. Conf. Comput. Linguist. (Association for Computational Linguistics, Santa Fe, New
Mexico, USA, 2018), pp. 1638–1649.
98. S. Bratus, A. Rumshisky, A. Khrabrov, R. Magar and P. Thompson, Domain-specific entity extraction from noisy, unstructured data using ontology-guided search, Int. J. Doc. Anal. Recognit.
IJDAR 14(2) (2011) 201–211.
99. X. Guo, H. Zhou, J. Su, X. Hao, Z. Tang, L. Diao and L. Li, Chinese agricultural diseases and pests
named entity recognition with multi-scale local context features and self-attention mechanism,
Comput. Electron. Agric. 179 (2020) 105830.
100. C. Liu, C. Fan, Z. Wang and Y. Sun, An instance transfer-based approach using enhanced recurrent neural network for domain named entity recognition, IEEE Access 8 (2020) 45263–45270.
101. J. Kim, Y. Ko and J. Seo, Construction of machine-labeled data for improving named entity
recognition by transfer learning, IEEE Access 8 (2020) 59684–59693.
102. J. Kim, Y. Ko and J. Seo, A bootstrapping approach with crf and deep learning models for improving the biomedical named entity recognition in multi-domains, IEEE Access 7 (2019) 70308–
70318.
103. X. Seti, A. Wumaier, T. Yibulayin, D. Paerhati, L. Wang and A. Saimaiti, Named-entity recognition in sports field based on a character-level graph convolutional network, Information 11(1)
(2020) 30.
104. F. Liao, L. Ma, J. Pei and L. Tan, Combined self-attention mechanism for Chinese named entity
recognition in military, Future Internet 11(8) (2019) 180.
105. S. Johnson, S. Shen and Y. Liu, CWPC_BiAtt: character–word–position combined bilstm-attention
for Chinese named entity recognition, Information 11(1) (2020) 45.
106. Y. Jin, J. Xie, W. Guo, C. Luo, D. Wu and R. Wang, LSTM-CRF neural network with gated self
attention for Chinese NER, IEEE Access 7 (2019) 136694–136703.
107. A. Ekbal and S. Saha, Multiobjective optimization for classifier ensemble and feature selection:
an application to named entity recognition, Int. J. Doc. Anal. Recognit. IJDAR 15(2) (2012)
143–166.
108. J. Xu, H. He, X. Sun, X. Ren and S. Li, Cross-domain and semisupervised named entity recognition in Chinese social media: a unified model, IEEEACM Trans. Audio Speech Lang. Process.
26(11) (2018) 2142–2152.
109. Y. Song, S. Jeong and H. Kim, Semi-automatic construction of a named entity dictionary for
entity-based sentiment analysis in social media, Multimed. Tools Appl. 76(9) (2017) 11319–
11329.
110. M. Song, W. C. Kim, D. Lee, G. E. Heo and K. Y. Kang, PKDE4J: entity and relation extraction
for public knowledge discovery, J. Biomed. Inform. 57 (2015) 320–332.
111. G. Crichton, S. Pyysalo, B. Chiu and A. Korhonen, A neural network multi-task learning approach
to biomedical named entity recognition, BMC Bioinformatics 18(1) (2017) 368.
112. X. Dong, S. Chowdhury, L. Qian, X. Li, Y. Guan, J. Yang and Q. Yu, deep learning for named
entity recognition on chinese electronic medical records: combining deep transfer learning with
multitask bi-directional LSTM RNN, PLOS ONE 14(5) (2019) e0216046.
113. X. Li, C. Fu, R. Zhong, D. Zhong, T. He and X. Jiang, A hybrid deep learning framework for
bacterial named entity recognition with domain features, BMC Bioinformatics 20(S16) (2019)
583.
114. M. Gao, Q. Xiao, S. Wu and K. Deng, An improved method for named entity recognition and its
application to CEMR, Future Internet 11(9) (2019) 185.
115. E. Batbaatar and K. H. Ryu, Ontology-based healthcare named entity recognition from twitter
messages using a recurrent neural network approach, Int. J. Environ. Res. Public. Health 16(19)
(2019) 3628.
116. X. Chen, C. Ouyang, Y. Liu and Y. Bu, Improving the named entity recognition of Chinese electronic medical records by combining domain dictionary and rules, Int. J. Environ. Res. Public.
Health 17(8) (2020) 2687.
117. Y. Wang, S. Ananiadou and J. Tsujii, Improving clinical named entity recognition in Chinese
using the graphical and phonetic feature, BMC Med. Inform. Decis. Mak. 19(S7) (2019) 273.
118. K. Xu, Z. Zhou, T. Gong, T. Hao and W. Liu, SBLC: a hybrid model for disease named entity
recognition based on semantic bidirectional LSTMs and conditional random fields, BMC Med.
Inform. Decis. Mak. 18(S5) (2018) 114.
119. C. Lyu, B. Chen, Y. Ren and D. Ji, Long short-term memory RNN for biomedical named entity
recognition, BMC Bioinformatics 18(1) (2017) 462.
120. B. Ji, R. Liu, S. Li, J. Yu, Q. Wu, Y. Tan and J. Wu, A hybrid approach for named entity recognition in Chinese electronic medical record, BMC Med. Inform. Decis. Mak. 19(S2) (2019)
64.
121. S. Chowdhury, X. Dong, L. Qian, X. Li, Y. Guan, J. Yang and Q. Yu, A multitask bi-directional
RNN model for named entity recognition on Chinese electronic medical records, BMC Bioinformatics 19(S17) (2018) 499.
122. R. Patra and S. K. Saha, A Kernel-based approach for biomedical named entity recognition, Sci.
World J. 2013 (2013) 1-7.
123. W. Yoon, C. H. So, J. Lee and J. Kang, CollaboNet: collaboration of deep neural networks for
biomedical named entity recognition, BMC Bioinformatics 20(S10) (2019) 249.
124. Z. Zhao, Z. Yang, L. Luo, L. Wang, Y. Zhang, H. Lin and J. Wang, Disease named entity recognition from biomedical literature using a novel convolutional neural network, BMC Med. Genomics
10(S5) (2017) 73.
125. Y. Lou, T. Qian, F. Li and D. Ji, A graph attention model for dictionary-guided named entity
recognition, IEEE Access 8 (2020) 71584–71592.
126. J. Qiu, Y. Zhou, Q. Wang, T. Ruan and J. Gao, Chinese clinical named entity recognition using residual dilated convolutional neural network with conditional random field, IEEE Trans.
NanoBioscience 18(3) (2019) 306–315.
127. K. Li, W. Ai, Z. Tang, F. Zhang, L. Jiang, K. Li and K. Hwang, Hadoop recognition of biomedical
named entity using conditional random fields, IEEE Trans. Parallel Distrib. Syst. 26(11) (2015)
3040–3051.
128. G. Wu, G. Tang, Z. Wang, Z. Zhang and Z. Wang, An attention-based BiLSTM-CRF model for
Chinese clinic named entity recognition, IEEE Access 7 (2019) 113942–113949.
129. D. Kim, J. Lee, C. H. So, H. Jeon, M. Jeong, Y. Choi, W. Yoon, M. Sung and J. Kang, A neural
named entity recognition and multi-type normalization tool for biomedical text mining, IEEE
Access 7 (2019) 73729–73740.
130. H. Wei, M. Gao, A. Zhou, F. Chen, W. Qu, C. Wang and M. Lu, Named entity recognition from
biomedical texts using a fusion attention-based BiLSTM-CRF, IEEE Access 7 (2019) 73627–
73636.
131. A. Casillas, N. Ezeiza, I. Goenaga, A. Pérez and X. Soto, Measuring the effect of different types
of unsupervised word representations on Medical Named Entity Recognition, Int. J. Med. Inf.
129 (2019) 100–106.
132. J. Chae, Y. Jung, T. Lee, S. Jung, C. Huh, G. Kim, H. Kim and H. Oh, Identifying non-elliptical
entity mentions in a coordinated NP with ellipses, J. Biomed. Inform. 47 (2014) 139–152.
133. Y. Chen, C. Zhou, T. Li, H. Wu, X. Zhao, K. Ye and J. Liao, Named entity recognition from
Chinese adverse drug event reports with lexical feature based BiLSTM-CRF and tri-training, J.
Biomed. Inform. 96 (2019) 103252.
134. M. Cho, J. Ha, C. Park and S. Park, Combinatorial feature embedding based on CNN and LSTM
for biomedical named entity recognition, J. Biomed. Inform. 103 (2020) 103381.
135. M. Gridach, Character-level neural network for biomedical named entity recognition, J. Biomed.
Inform. 70 (2017) 85–91.
136. J. Guo, C. Blake and Y. Guan, Evaluating automated entity extraction with respect to drug and
non-drug treatment strategies, J. Biomed. Inform. 94 (2019) 103177.
137. I. J. Unanue, E. Z. Borzeshi and M. Piccardi, Recurrent neural networks with specialized word
embeddings for health-domain named-entity recognition, J. Biomed. Inform. 76 (2017) 102–109.
138. S. Santiso, A. Pérez, A. Casillas and M. Oronoz, Neural negated entity recognition in Spanish
electronic health records, J. Biomed. Inform. 105 (2020) 103419.
139. J. Urbain, Mining heart disease risk factors in clinical text with named entity recognition and
distributional semantic models, J. Biomed. Inform. 58 (2015) S143–S149.
140. Q. Wang, Y. Zhou, T. Ruan, D. Gao, Y. Xia and P. He, Incorporating dictionaries into deep neural
networks for the Chinese clinical named entity recognition, J. Biomed. Inform. 92 (2019) 103133.
141. M. Yin, C. Mou, K. Xiong and J. Ren, Chinese clinical named entity recognition with radical-level
feature and self-attention mechanism, J. Biomed. Inform. 98 (2019) 103289.
142. S. Zhang and N. Elhadad, Unsupervised biomedical named entity recognition: Experiments with
clinical and biological texts, J. Biomed. Inform. 46(6) (2013) 1088–1098.
143. S. Zhao, Z. Cai, H. Chen, Y. Wang, F. Liu and A. Liu, Adversarial training based lattice LSTM
for Chinese clinical named entity recognition, J. Biomed. Inform. 99 (2019) 103290.
144. A. Pérez, R. Weegar, A. Casillas, K. Gojenola, M. Oronoz and H. Dalianis, Semi-supervised
medical entity recognition: A study on Spanish and Swedish clinical corpora, J. Biomed. Inform.
71 (2017) 16–30.
145. A. Akkasi, E. Varoglu and N. Dimililer, Balanced undersampling: a novel sentence-based under- ˘
sampling method to improve recognition of named entities in chemical and biomedical text, Appl.
Intell. 48(8) (2018) 1965–1978.
146. M. Basaldella, L. Furrer, C. Tasso and F. Rinaldi, Entity recognition in the biomedical domain
using a hybrid approach, J. Biomed. Semant. 8(1) (2017) 51.
147. Y. Qin and Y. Zeng, Research of clinical named entity recognition based on Bi-LSTM-CRF, J.
Shanghai Jiaotong Univ. Sci. 23(3) (2018) 392–397.
148. B. Tang, X. Wang, J. Yan and Q. Chen, Entity recognition in Chinese clinical text using attentionbased CNN-LSTM-CRF, BMC Med. Inform. Decis. Mak. 19(S3) (2019) 74.
149. R. Weegar, A. Pérez, A. Casillas and M. Oronoz, Recent advances in Swedish and Spanish medical entity recognition in clinical texts using deep neural approaches, BMC Med. Inform. Decis.
Mak. 19(S7) (2019) 274.
150. L. Yang and Y. Zhou, Exploring feature sets for two-phase biomedical named entity recognition
using semi-CRFs, Knowl. Inf. Syst. 40(2) (2014) 439–453.
151. L. Yao, C. Sun, Y. Wu, X. Wang and X. Wang, Biomedical named entity recognition using generalized expectation criteria, Int. J. Mach. Learn. Cybern. 2(4) (2011) 235–243.
152. S. M. Yimam, C. Biemann, L. Majnaric, Š. Šabanovic and A. Holzinger, An adaptive annotation ´
approach for biomedical entity and relation recognition, Brain Inform. 3(3) (2016) 157–168.
153. M. Liu, Y. Zhang, W. Li and D. Ji, Joint model of entity recognition and relation extraction with
self-attention mechanism, ACM Trans. Asian Low-Resour. Lang. Inf. Process. 19(4) (2020)
1–19.
154. R. Catelli, F. Gargiulo, V. Casola, G. De Pietro, H. Fujita and M. Esposito, Crosslingual named
entity recognition for clinical de-identification applied to a COVID-19 Italian data set, Appl. Soft
Comput. 97 (2020) 106779.
155. Y. Song, S. Tian and L. Yu, A method for identifying local drug names in Xinjiang based on
BERT-BiLSTM-CRF, Autom. Control Comput. Sci. 54(3) (2020) 179–190.
156. Y. Tian, W. Shen, Y. Song, F. Xia, M. He and K. Li, Improving biomedical named entity recognition with syntactic information, BMC Bioinformatics 21(1) (2020) 539.
157. Y.-M. Kim and T.-H. Lee, Korean clinical entity recognition from diagnosis text using BERT,
BMC Med. Inform. Decis. Mak. 20(S7) (2020) 242.
158. L. Akhtyamova, P. Martinez, K. Verspoor and J. Cardiff, Testing contextualized word embeddings
to improve NER in Spanish clinical case narratives, IEEE Access 8 (2020) 164717–164726.
159. D. Zhang, C. Xia, C. Xu, Q. Jia, S. Yang, X. Luo and Y. Xie, Improving distantly-supervised
named entity recognition for traditional Chinese medicine text via a novel back-labeling approach,
IEEE Access 8 (2020) 145413–145421.
160. G. Wen, H. Chen, H. Li, Y. Hu, Y. Li and C. Wang, Cross domains adversarial learning for Chinese
named entity recognition for online medical consultation, J. Biomed. Inform. 112 (2020) 103608.
161. C. Wang, H. Wang, H. Zhuang, W. Li, S. Han, H. Zhang and L. Zhuang, Chinese medical named
entity recognition based on multi-granularity semantic dictionary and multimodal tree, J. Biomed.
Inform. 111 (2020) 103583.
162. C. Wu, G. Luo, C. Guo, Y. Ren, A. Zheng and C. Yang, An attention-based multi-task model
for named entity recognition and intent analysis of Chinese online medical questions, J. Biomed.
Inform. 108 (2020) 103511.
163. B. Ji, S. Li, J. Yu, J. Ma, J. Tang, Q. Wu, Y. Tan, H. Liu and Y. Ji, Research on Chinese medical
named entity recognition based on collaborative cooperation of multiple neural network models,
J. Biomed. Inform. 104 (2020) 103395.
164. I. Lerner, N. Paris and X. Tannier, Terminologies augmented recurrent neural network model for
clinical named entity recognition, J. Biomed. Inform. 102 (2020) 103356.
165. T. Iwakura, H. Takamura and M. Okumura, A named entity recognition method based on decomposition and concatenation of word chunks, ACM Trans. Asian Lang. Inf. Process. 12(3) (2013)
1–18.
166. D. Deng, G. Li, J. Feng, Y. Duan and Z. Gong, A unified framework for approximate dictionarybased entity extraction, VLDB J. 24(1) (2015) 143–167.
167. R. Murthy, M. M. Khapra and P. Bhattacharyya, Improving NER tagging performance in lowresource languages via multilingual learning, ACM Trans. Asian Low-Resour. Lang. Inf. Process.
18(2) (2019) 1–20.
168. L. Li, P. Wang, D. Huang and L. Zhao, Mining English-Chinese named entity pairs from comparable corpora, ACM Trans. Asian Lang. Inf. Process. 10(4) (2011) 1–19.
169. L. Zhao, X. Qiu, Q. Zhang and X. Huang, Sequence labeling with deep gated dual path CNN,
IEEEACM Trans. Audio Speech Lang. Process. 27(12) (2019) 2326–2335.
170. H. Kim and H. Kim, Integrated model for morphological analysis and named entity recognition
based on label attention networks in Korean, Appl. Sci. 10(11) (2020) 3740.
171. W. Khan, A. Daud, F. Alotaibi, N. Aljohani and S. Arafat, Deep recurrent neural networks with
word embeddings for Urdu named entity recognition, ETRI J. 42(1) (2020) 90–100.
172. M. Ali, G. Tan and A. Hussain, Bidirectional recurrent neural network approach for Arabic named
entity recognition, Future Internet 10(12) (2018) 123.
173. A. Saimaiti, L. Wang and T. Yibulayin, Learning subword embedding to improve Uyghur namedentity recognition, Information 10(4) (2019) 139.
174. A. P. Ajees, K. Manju and S. Mary Idicula, An improved word representation for deep learning
based NER in Indian languages, Information 10(6) (2019) 186.
175. B. Peng, J. Wu, H. Yuan, Q. Guo and D. Tao, ANEEC: A quasi-automatic system for massive
named entity extraction and categorization, Comput. J. 56(11) (2013) 1328–1346.
176. H. Zhu, C. He, Y. Fang and W. Xiao, Fine grained named entity recognition via Seq2seq framework, IEEE Access 8 (2020) 53953–53961.
177. M. Al-Smadi, S. Al-Zboon, Y. Jararweh and P. Juola, Transfer learning for Arabic named entity
recognition with deep neural networks, IEEE Access 8 (2020) 37736–37745.
178. I. Ashrafi, M. Mohammad, A. S. Mauree, G. Md. A. Nijhum, R. Karim, N. Mohammed and
S. Momen, Banner: A cost-sensitive contextualized model for Bangla named entity recognition,
IEEE Access 8 (2020) 58206–58226.
179. D. Li, X. Zhang and X. Wu, Integrated Chinese segmentation, parsing and named entity recognition, Chin. J. Electron. 27(4) (2018) 756–760.
180. M. N. A. Ali, G. Tan and A. Hussain, Boosting Arabic named-entity recognition with multiattention layer, IEEE Access 7 (2019) 46575–46582.
181. R. Agerri and G. Rigau, Robust multilingual named entity recognition with shallow semi-supervised
features, Artif. Intell. 238 (2016) 63–82.
182. G. Bekoulis, J. Deleu, T. Demeester and C. Develder, Joint entity recognition and relation extraction as a multi-head selection problem, Expert Syst. Appl. 114 (2018) 34–45.
183. E. Fersini, E. Messina, G. Felici and D. Roth, Soft-constrained inference for named entity recognition, Inf. Process. Manag. 50(5) (2014) 807–819.
184. O. Ghiasvand and R. J. Kate, Learning for clinical named entity recognition without manual
annotations, Inform. Med. Unlocked 13 (2018) 122–127.
185. S.-H. Na, H. Kim, J. Min and K. Kim, Improving LSTM CRFs using character-based compositions for Korean named entity recognition, Comput. Speech Lang. 54 (2019) 106–121.
186. M. Konkol, T. Brychcín and M. Konopík, Latent semantics in named entity recognition, Expert
Syst. Appl. 42(7) (2015) 3470–3479.
187. M. N. A. Ali and G. Tan, Bidirectional encoder–decoder model for Arabic named entity recognition, Arab. J. Sci. Eng. 44(11) (2019) 9693–9701.
188. H. Al-Jumaily, P. Martínez, J. L. Martínez-Fernández and E. Van der Goot, A real time named
entity recognition system for Arabic text mining, Lang. Resour. Eval. 46(4) (2012) 543–563.
189. K.-Y. Cui, P.-J. Ren, Z.-M. Chen, T. Lian and J. Ma, Relation enhanced neural model for type
classification of entity mentions with a fine-grained taxonomy, J. Comput. Sci. Technol. 32(4)
(2017) 814–827.
190. C. de Pablo-Sánchez, I. Segura-Bedmar, P. Martínez and A. Iglesias-Maqueda, Lightly supervised
acquisition of named entities and linguistic patterns for multilingual text mining, Knowl. Inf. Syst.
35(1) (2013) 87–109.
191. B. Desmet and V. Hoste, Fine-grained Dutch named entity recognition, Lang. Resour. Eval. 48(2)
(2014) 307–343.
192. Z. Li, D. Feng, D. Li and X. Lu, Learning to select pseudo labels: a semi-supervised method for
named entity recognition, Front. Inf. Technol. Electron. Eng. 21(6) (2020) 903–916.
193. R. Lima, B. Espinasse and F. Freitas, OntoILPER: an ontology- and inductive logic programmingbased system to extract entities and relations from text, Knowl. Inf. Syst. 56(1) (2018) 223–255.
194. H. Moradi, F. Ahmadi and M.-R. Feizi-Derakhshi, A hybrid approach for Persian named entity
recognition, Iran. J. Sci. Technol. Trans. Sci. 41(1) (2017) 215–222.
195. D. Peng, Y. Wang, C. Liu and Z. Chen, TL-NER: a transfer learning model for Chinese named
entity recognition, Inf. Syst. Front. 22(6) (2020) 1291–1304.
196. R. Sharma, S. Morwal, B. Agarwal, R. Chandra and M. S. Khan, A deep neural network-based
model for named entity recognition for Hindi language, Neural Comput. Appl. 32(20) (2020)
16191–16203.
197. M. Song, H. Yu and W.-S. Han, Developing a hybrid dictionary-based bio-entity recognition
technique, BMC Med. Inform. Decis. Mak. 15(S1) (2015) S9.
198. Z. Wang, X. Cui, L. Gao, Q. Yin, L. Ke and S. Zhang, A hybrid model of sentimental entity
recognition on mobile social media, EURASIP J. Wirel. Commun. Netw. 2016(1) (2016) 253.
199. C.-L. Chou, C.-H. Chang, Y.-H. Lin and K.-C. Chien, On the construction of web NER model
training tool based on distant supervision, ACM Trans. Asian Low-Resour. Lang. Inf. Process.
19(6) (2020) 1–28.
200. M. S. H. Ameur, R. Belkebir and A. Guessoum, Robust Arabic text categorization by combining
convolutional and recurrent neural networks, ACM Trans. Asian Low-Resour. Lang. Inf. Process.
19(5) (2020) 1–16.
201. N. Alshammari and S. Alanazi, The impact of using different annotation schemes on named entity
recognition, Egypt. Inform. J. 22(3) (2021) 295–302.
202. J. T. Zhou, H. Zhang, D. Jin, X. Peng, Y. Xiao and Z. Cao, RoSeq: robust sequence labeling,
IEEE Trans. Neural Netw. Learn. Syst. (2019) 1–11.
203. C. Gong, Z. Li, Q. Xia, W. Chen and M. Zhang, Hierarchical LSTM with char-subword-word
tree-structure representation for Chinese named entity recognition, Sci. China Inf. Sci. 63(10)
(2020) 202102.
204. M. Li, T. Munkhdalai, X. Yu and K. H. Ryu, A novel approach for protein-named entity recognition and protein-protein interaction extraction, Math. Probl. Eng. 2015 (2015) 1–10.
205. F. Zhu and B. Shen, Combined SVM-CRFs for biological named entity recognition with maximal
bidirectional squeezing, PLoS ONE 7(6) (2012) e39230.
206. H. Zhou, S. Ning, Z. Liu, C. Lang, Z. Liu and B. Lei, Knowledge-enhanced biomedical named
entity recognition and normalization: application to proteins and genes, BMC Bioinformatics
21(1) (2020) 35.
207. R. Danger, F. Pla, A. Molina and P. Rosso, Towards a protein–protein interaction information
extraction system: recognizing named entities, Knowl.-Based Syst. 57 (2014) 104–118.
208. H. Fei, Y. Ren and D. Ji, Dispatched attention with multi-task learning for nested mention recognition, Inf. Sci. 513 (2020) 241–251.
209. A. Lourenço, M. Conover, A. Wong, A. Nematzadeh, F. Pan, H. Shatkay and L. M. Rocha, Correction: a linear classifier based on entity recognition tools and a statistical approach to method
extraction in the protein-protein interaction literature, BMC Bioinformatics 13(1) (2012) 180.
210. X. Wang, Y. Li, T. He, X. Jiang and X. Hu, Recognition of bacteria named entity using conditional
random fields in Spark, BMC Syst. Biol. 12(S6) (2018) 106.
211. H. Zhang, Y. Guo and T. Li, Multifeature named entity recognition in information security based
on adversarial learning, Secur. Commun. Netw. 2019 (2019) 1–9.
212. F. Yi, B. Jiang, L. Wang and J. Wu, Cybersecurity named entity recognition using multi-modal
ensemble learning, IEEE Access 8 (2020) 63214–63224.
213. M. W. Al-Nabki, E. Fidalgo, E. Alegre and L. Fernández-Robles, Improving named entity recognition in noisy user-generated text with local distance neighbor feature, Neurocomputing 382
(2020) 1–11.
214. G. Kim, C. Lee, J. Jo and H. Lim, Automatic extraction of named entities of cyber threats using a
deep Bi-LSTM-CRF network, Int. J. Mach. Learn. Cybern. 11(10) (2020) 2341–2355.
215. M. M. Tikhomirov, N. V. Loukachevitch and B. V. Dobrov, Recognizing named entities in specific
domain, Lobachevskii J. Math. 41(8) (2020) 1591–1602.
216. P. Ma, B. Jiang, Z. Lu, N. Li and Z. Jiang, Cybersecurity named entity recognition using bidirectional long short-term memory with conditional random fields, Tsinghua Sci. Technol. 26(3)
(2021) 259–265.
217. T. Munkhdalai, M. Li, K. Batsuren, H. A. Park, N. H. Choi and K. H. Ryu, Incorporating domain
knowledge in chemical and biomedical named entity recognition with word representations, J.
Cheminformatics 7(S1) (2015) S9.
218. Y. Chen, Y. Hu, Y. Li, R. Huang, Y. Qin, Y. Wu, Q. Zheng and P. Chen, A Boundary assembling
method for nested biomedical named entity recognition, IEEE Access 8 (2020) 214141–214152.
219. S. Dadas and J. Protasiewicz, A bidirectional iterative algorithm for nested named entity recognition, IEEE Access 8 (2020) 135091–135102.
220. S. Gajendran, D. Manjula and V. Sugumaran, Character level and word level embedding with
bidirectional LSTM – dynamic recurrent neural network for biomedical named entity recognition
from literature, J. Biomed. Inform. 112 (2020) 103609.
221. P. Corbett and J. Boyle, Chemlistem: chemical named entity recognition using recurrent neural
networks, J. Cheminformatics 10(1) (2018) 59.
222. B. Kolluru, L. Hawizy, P. Murray-Rust, J. Tsujii and S. Ananiadou, Using workflows to explore
and optimise named entity recognition for chemistry, PLoS ONE 6(5) (2011) e20181.
223. I. Korvigo, M. Holmatov, A. Zaikovskii and M. Skoblov, Putting hands to rest: efficient deep
CNN-RNN architecture for chemical named entity recognition with no hand-crafted rules, J.
Cheminformatics 10(1) (2018) 28.
224. L. Luo, Z. Yang, P. Yang, Y. Zhang, L. Wang, J. Wang and H. Lin, A neural network approach to
chemical and gene/protein entity recognition in patents, J. Cheminformatics 10(1) (2018) 65.
225. Y. Yao and A. Sun, Mobile phone name extraction from internet forums: a semi-supervised approach, World Wide Web 19(5) (2016) 783–805.
226. R. Fan, L. Wang, J. Yan, W. Song, Y. Zhu and X. Chen, Deep learning-based named entity
recognition and knowledge graph construction for geological hazards, ISPRS Int. J. Geo-Inf.
9(1) (2019) 15.
227. H. Huang, H. Wang and D. Jin, A low-cost named entity recognition research based on active
learning, Sci. Program. 2018 (2018) 1–10.
228. M. Zhang, G. Geng and J. Chen, Semi-supervised bidirectional long short-term memory and
conditional random fields model for named-entity recognition using embeddings from language
models representations, Entropy 22(2) (2020) 252.
229. C. Yan, Q. Su and J. Wang, MoGCN: Mixture of gated convolutional neural network for named
entity recognition of Chinese historical texts, IEEE Access 8 (2020) 181629–181639.
230. M. Shardlow, M. Ju, M. Li, C. O’Reilly, E. Iavarone, J. McNaught and S. Ananiadou, A text
mining pipeline using active and deep learning aimed at curating information in computational
neuroscience, Neuroinformatics 17(3) (2019) 391–406.
231. A. Rajkomar, J. Dean and I. Kohane, Machine learning in medicine, N. Engl. J. Med. 380(14)
(2019) 1347–1358.
232. Y. Wang, Z. Yu, L. Chen, Y. Chen, Y. Liu, X. Hu and Y. Jiang, Supervised methods for symptom
name recognition in free-text clinical records of traditional Chinese medicine: an empirical study,
J. Biomed. Inform. 47 (2014) 91–104.
233. T. H. Truong, M. H. Dao and D. Q. Nguyen, COVID-19 named entity recognition for Vietnamese,
in Proc. 2021 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol.
(Association for Computational Linguistics, Online, 2021), pp. 2146–2153.
234. N. Colic, L. Furrer and F. Rinaldi, Annotating the pandemic: named entity recognition and normalisation in COVID-19 literature, in Proc. 1st Workshop NLP COVID-19 Part 2 EMNLP 2020
(Association for Computational Linguistics, Online, 2020).
235. X. Wang, X. Song, B. Li, Y. Guan and J. Han, Comprehensive named entity recognition on
CORD-19 with distant or weak supervision, ArXiv200312218 Cs (2020).
236. O. Levy, Y. Goldberg and I. Dagan, Improving distributional similarity with lessons learned from
word embeddings, Trans. Assoc. Comput. Linguist. 3 (2015) 211–225.
237. G. Lev, B. Klein and L. Wolf, In defense of word embedding for generic text representation,
in Natural Language Processing and Information Systems, eds. C. Biemann, S. Handschuh, A.
Freitas, F. Meziane and E. Métais (Springer International Publishing, Cham, 2015), pp. 35–50.
238. M. Li, Q. Lu, Y. Long and L. Gui, Inferring affective meanings of words from word embedding,
IEEE Trans. Affect. Comput. 8 (2017) 443–456.
239. U. Kamath, J. Liu and J. Whitaker, Deep Learning for NLP and Speech Recognition (Springer
International Publishing, Cham, 2019).
240. F. M. Hasan, N. UzZaman and M. Khan, Comparison of different POS tagging techniques (ngram, HMM and Brill’s tagger) for Bangla, in Adv. Innov. Syst. Comput. Sci. Softw. Eng., ed. K.
Elleithy (Springer Netherlands, Dordrecht, 2007), pp. 121–126.
241. A. A. Farzindar and D. Inkpen, Natural language processing for social media, third edition, Synth.
Lect. Hum. Lang. Technol. 13(2) (2020) 1–219.
242. B. Y. Lin, F. Xu, Z. Luo and K. Zhu, Multi-channel BiLSTM-CRF model for emerging named
entity recognition in social media, in Proc. 3rd Workshop Noisy User-Gener. Text (Association
for Computational Linguistics, Copenhagen, Denmark, 2017), pp. 160–165.
243. X. Li, H. Zhang and X.-H. Zhou, Chinese clinical named entity recognition with variant neural
structures based on BERT methods, J. Biomed. Inform. 107 (2020) 103422.
244. C. Sun and Z. Yang, Transfer learning in biomedical named entity recognition: an evaluation of
BERT in the PharmaCoNER task, in Proc. 5th Workshop BioNLP Open Shar. Tasks (Association
for Computational Linguistics, Hong Kong, China, 2019), pp. 100–104.
245. J. Gao, M. Li, A. Wu and C.-N. Huang, Chinese word segmentation and named entity recognition:
a pragmatic approach, Comput. Linguist. 31(4) (2005) 531–574.
246. A. Krizhevsky, I. Sutskever and G. E. Hinton, ImageNet classification with deep convolutional
neural networks, Commun. ACM 60(6) (2017) 84–90.
247. S. Francis, J. V. Landeghem and M.-F. Moens, Transfer learning for named entity recognition in
financial and biomedical documents, Information 10(8) (2019) 248.
248. J. D. Lafferty, A. McCallum and F. C. N. Pereira, Conditional random fields: probabilistic models
for segmenting and labeling sequence data, in Proc. Eighteenth Int. Conf. Mach. Learn. (Morgan
Kaufmann Publishers Inc., San Francisco, CA, USA, 2001), pp. 282–289.
249. S. Ananiadou, D. Sullivan, W. Black, G.-A. Levow, J. J. Gillespie, C. Mao, S. Pyysalo, B. Kolluru,
J. Tsujii and B. Sobral, Named entity recognition for bacterial type IV secretion systems, PLoS
ONE 6(3) (2011) e14780.
250. Y. Tsuruoka, J. Tsujii and S. Ananiadou, Fast full parsing by linear-chain conditional random
fields, in Proc. 12th Conf. Eur. Chapter ACL EACL 2009 (Association for Computational Linguistics, Athens, Greece, 2009), pp. 790–798.
251. Y. Qin, G. Shen, W. Zhao, Y. Chen, M. Yu and X. Jin, A network security entity recognition
method based on feature template and CNN-BiLSTM-CRF, Front. Inf. Technol. Electron. Eng.
20(6) (2019) 872–884.
252. N. Greenberg, T. Bansal, P. Verga and A. McCallum, Marginal likelihood training of BiLSTMCRF for biomedical named entity recognition from disjoint label sets, in Proc. 2018 Conf. Empir.
Methods Nat. Lang. Process. (Association for Computational Linguistics, Brussels, Belgium,
2018), pp. 2824–2829.
253. S. Misawa, M. Taniguchi, Y. Miura and T. Ohkuma, Character-based bidirectional LSTM-CRF
with words and characters for Japanese Named entity recognition, in Proc. First Workshop Subword Character Level Models NLP (Association for Computational Linguistics, Copenhagen,
Denmark, 2017), pp. 97–102.
254. W. Wang, C. Xiao, X. Lin and C. Zhang, Efficient approximate entity extraction with edit distance
constraints, in Proc. 2009 ACM SIGMOD Int. Conf. Manag. Data (Association for Computing
Machinery, New York, NY, USA, 2009), pp. 759–770.
255. K. Chakrabarti, S. Chaudhuri, V. Ganti and D. Xin, An efficient filter for approximate membership
checking, in Proc. 2008 ACM SIGMOD Int. Conf. Manag. Data (Association for Computing
Machinery, New York, NY, USA, 2008), pp. 805–818.
256. D. T. Larose, Discovering Knowledge in Data: An Introduction to Data Mining (John Wiley &
Sons, Inc., Hoboken, NJ, USA, 2004).
257. M. Althobaiti, U. Kruschwitz and M. Poesio, Combining minimally-supervised methods for Arabic named entity recognition, Trans. Assoc. Comput. Linguist. 3 (2015) 243–255.
258. P. Cao, Y. Chen, K. Liu, J. Zhao and S. Liu, Adversarial transfer learning for Chinese named
entity recognition with self-attention mechanism, in Proc. 2018 Conf. Empir. Methods Nat.
Lang. Process. (Association for Computational Linguistics, Brussels, Belgium, 2018), pp. 182–
192.
259. M. Riedl and S. Padó, A named entity recognition shootout for German, in Proc. 56th Annu.
Meet. Assoc. Comput. Linguist. Vol. 2 Short Pap. (Association for Computational Linguistics,
Melbourne, Australia, 2018), pp. 120–125.
260. W. Lee, K. Kim, E. Y. Lee and J. Choi, Conditional random fields for clinical named entity
recognition: a comparative study using Korean clinical texts, Comput. Biol. Med. 101 (2018)
7–14.
261. D. C. Wintaka, M. A. Bijaksana and I. Asror, Named-entity recognition on Indonesian tweets
using bidirectional LSTM-CRF, Procedia Comput. Sci. 157 (2019) 221–228.
262. R. M. Syachrul M.A.K., M. A. Bijaksana and A. F. Huda, Person entity recognition for the Indonesian Qur’an translation with the approach hidden Markov Model-Viterbi, Procedia Comput.
Sci. 157 (2019) 214–220.
263. F. Y. Azalia, M. A. Bijaksana and A. F. Huda, Name Indexing in Indonesian translation of hadith
using named entity recognition with naïve Bayes classifier, Procedia Comput. Sci. 157 (2019)
142–149.
264. B. Wilie, K. Vincentio, G. I. Winata, S. Cahyawijaya, X. Li, Z. Y. Lim, S. Soleman, R. Mahendra, P. Fung, S. Bahar and A. Purwarianti, IndoNLU: benchmark and resources for evaluating
Indonesian natural language understanding, ArXiv200905387 Cs (2020).
265. A. S. Wibawa and A. Purwarianti, Indonesian named-entity recognition for 15 classes using ensemble supervised learning, Procedia Comput. Sci. 81 (2016) 221–228.
266. W. Gunawan, D. Suhartono, F. Purnomo and A. Ongko, Named-entity recognition for Indonesian
language using bidirectional LSTM-CNNs, Procedia Comput. Sci. 135 (2018) 425–432.
267. J. Xie, Z. Yang, G. Neubig, N. A. Smith and J. Carbonell, Neural cross-lingual named entity
recognition with minimal resources, ArXiv180809861 Cs (2018).
268. C. Sabty, M. Elmahdy and S. Abdennadher, Named entity recognition on Arabic-English codemixed data, in 2019 IEEE 13th Int. Conf. Semantic Comput. ICSC (IEEE, Newport Beach, CA,
USA, 2019), pp. 93–97.
269. A. Zafarian, A. Rokni, S. Khadivi and S. Ghiasifard, Semi-supervised learning for named entity
recognition using weakly labeled training data, in 2015 Int. Symp. Artif. Intell. Signal Process.
AISP (IEEE, Mashhad, Iran, 2015), pp. 129–135.
270. G. I. Winata, C.-S. Wu, A. Madotto and P. Fung, Bilingual character representation for efficiently
addressing out-of-vocabulary words in code-switching named entity recognition, in Proc. Third
Workshop Comput. Approaches Linguist. Code-Switch. (Association for Computational Linguistics, Melbourne, Australia, 2018), pp. 110–114.
271. L. H. B. Nguyen, D. Dinh and P. Tran, An approach to construct a named entity annotated EnglishVietnamese bilingual corpus, ACM Trans. Asian Low-Resour. Lang. Inf. Process. 16 (2016)
1–17.
272. T.-A. Dao, H.-T. Truong, L. Nguyen and D. Dinh, Building a named entity annotated bilingual
English-Vietnamese corpus, in 2018 10th Int. Conf. Knowl. Syst. Eng. KSE (IEEE, Ho Chi Minh
City, 2018), pp. 61–66.
273. M. Arkhipov, M. Trofimova, Y. Kuratov and A. Sorokin, Tuning multilingual transformers for
language-specific named entity recognition, in Proc. 7th Workshop Balto-Slav. Nat. Lang. Process. (Association for Computational Linguistics, Florence, Italy, 2019), pp. 89–93.
274. J. Ni and R. Florian, Improving multilingual named entity recognition with wikipedia entity type
mapping, in Proc. 2016 Conf. Empir. Methods Nat. Lang. Process. (Association for Computational Linguistics, Austin, Texas, 2016), pp 1275–1284.
Aggarwal and C. Zhai (Springer US, Boston, MA, 2012), pp. 1–10.
2. J. Jiang, Information extraction from text, in Mining Text Data, eds. C. C. Aggarwal and C. Zhai
(Springer US, Boston, MA, 2012), pp. 11–41.
3. J. Piskorski and R. Yangarber, Information extraction: past, present and future, in Multi-source,
Multilingual Information Extraction and Summarization, eds. T. Poibeau, H. Saggion, J. Piskorski and R. Yangarber (Springer, Berlin, Heidelberg, 2013), pp. 23–49.
4. I. Augenstein, L. Derczynski and K. Bontcheva, Generalisation in named entity recognition: a
quantitative analysis, Comput. Speech Lang. 44 (2017) 61–83.
5. A. Goyal, V. Gupta and M. Kumar, Analysis of different supervised techniques for named entity
recognition, in Adv. Inform. Comput. Res., eds. A. K. Luhach, D. S. Jat, K. B. G. Hawari, X.-Z.
Gao and P. Lingras (Springer, Singapore, 2019), pp. 184–195.
6. Appendix C: Named entity task definition (v2.1), in Sixth Message Underst. Conf. MUC-6 Proc.
Conf. Held Columbia Md. Novemb. 6-8 1995 (Columbia, 1995).
7. M. Marrero, J. Urbano, S. Sánchez-Cuadrado, J. Morato and J. M. Gómez-Berbís, Named entity
recognition: fallacies, challenges and opportunities, Comput. Stand. Interfaces 35(5) (2013)
482–489.
8. M. S. Salleh, S. A. Asmai, H. Basiron and S. Ahmad, A Malay named entity recognition using
conditional random fields, in 2017 5th Int. Conf. Inf. Commun. Technol. ICoIC7 (IEEE, Melaka,
Malaysia, 2017), pp. 1–6.
9. L. Derczynski, D. Maynard, G. Rizzo, M. van Erp, G. Gorrell, R. Troncy, J. Petrak and K.
Bontcheva, Analysis of named entity recognition and linking for tweets, Inf. Process. Manag.
51(2) (2015) 32–49.
10. S. Eltyeb and N. Salim, Chemical named entities recognition: a review on approaches and applications, J. Cheminformatics 6 (2014) 17.
11. D. Nadeau, P. D. Turney and S. Matwin, Unsupervised named-entity recognition: generating
gazetteers and resolving ambiguity, in Advances in Artificial Intelligence, eds. A. Y. Tawfik and
S. D. Goodwin (Springer Berlin Heidelberg, Berlin, Heidelberg, 2006), pp. 266–277.
12. S. J. Pan, Z. Toh and J. Su, Transfer joint embedding for cross-domain named entity recognition,
ACM Trans. Inf. Syst. 31(2) (2013) 1–27.
13. L. Li, W. Fan and D. Huang, A two-phase Bio-NER system based on integrated classifiers and
multiagent strategy, IEEE/ACM Trans. Comput. Biol. Bioinform. 10(4) (2013) 897–904.
14. A. Akkasi and E. Varoglu, Improving biochemical named entity recognition using pso classifier
selection and bayesian combination methods, IEEE/ACM Trans. Comput. Biol. Bioinform. 14(6)
(2017) 1327–1338.
15. Y. Wen, C. Fan, G. Chen, X. Chen and M. Chen, A survey on named entity recognition, in
Communications, Signal Processing, and Systems, eds. Q. Liang, W. Wang, X. Liu, Z. Na, M. Jia
and B. Zhang (Springer Singapore, Singapore, 2020), pp. 1803–1810.
16. Y. Chen, T. A. Lasko, Q. Mei, J. C. Denny and H. Xu, A study of active learning methods for
named entity recognition in clinical text, J. Biomed. Inform. 58 (2015) 11–18.
17. J. A. Lossio-Ventura, C. Jonquet, M. Roche and M. Teisseire, Biomedical term extraction: overview
and a new methodology, Inf. Retr. J. 19(1-2) (2016) 59–99.
18. U. Kanimozhi and D. Manjula, A systematic review on biomedical named entity recognition,
in Data Science Analytics and Applications, eds. S. R and M. Sharma (Springer Singapore,
Singapore, 2018), pp. 19–37.
19. G. Popovski, B. K. Seljak and T. Eftimov, A survey of named-entity recognition methods for food
information extraction, IEEE Access 8 (2020) 31586–31594.
20. D. Nadeau and S. Sekine, A survey of named entity recognition and classification, Lingvisticae
Investig. 30(1) (2007) 3–26
21. A. Goyal, V. Gupta and M. Kumar, Recent named entity recognition and classification techniques:
a systematic review, Comput. Sci. Rev. 29 (2018) 21–43.
22. V. T. Chakaravarthy, V. Pandit, S. Roy, P. Awasthi and M. K. Mohania, Decision trees for entity identification: approximation algorithms and hardness results, ACM Trans. Algorithms 7(2)
(2011) 1–22.
23. X. Liao and Z. Zhao, Unsupervised approaches for textual semantic annotation, a survey, ACM
Comput. Surv. 52(4) (2019) 1–45.
24. A. Thomas and S. Sangeetha, Deep learning architectures for named entity recognition: a survey,
in Adv. Comput. Intell. Eng., eds. B. Pati, C. R. Panigrahi, R. Buyya and K.-C. Li (Springer,
Singapore, 2020), pp. 215–225.
25. J. Li, A. Sun, J. Han and C. Li, A survey on deep learning for named entity recognition, IEEE
Trans. Knowl. Data Eng. (2020) 1–1.
26. A. Dandashi, J. Al Jaam and S. Foufou, Arabic named entity recognition—a survey and analysis,
in Intelligent Interactive Multimedia Systems and Services 2016, Series in Smart Innovation, Systems and Technologies, Vol.55, eds. G. Pietro, L. Gallo, R. Howlett and L. Jain (Springer, Cham,
2016), pp. 83–96.
27. K. Shaalan, A survey of Arabic named entity recognition and classification, Comput. Linguist.
40(2) (2014) 469–510.
28. B. A. Kitchenham, Systematic review in software engineering: where we are and where we should
be going, in Proc. 2nd Int. Workshop Evidential Assess. Softw. Technol. - EAST 12 (ACM Press,
Lund, Sweden, 2012), p. 1.
29. C. Okoli and K. Schabram, A guide to conducting a systematic literature review of information
systems research, SSRN Electron. J. (2010).
30. A. Ekbal and S. Saha, Stacked ensemble coupled with feature selection for biomedical entity
extraction, Knowl.-Based Syst. 46(2013) 22–32.
31. A. Ekbal and S. Saha, Simultaneous feature and parameter selection using multiobjective optimization: application to named entity recognition, Int. J. Mach. Learn. Cybern. 7(4) (2016)
597–611.
32. A. Ekbal and S. Saha, Combining feature selection and classifier ensemble using a multiobjective simulated annealing approach: application to named entity recognition, Soft Comput. 17(1)
(2013) 1–16.
33. A. Ekbal, S. Saha and U. K. Sikdar, Biomedical named entity extraction: some issues of corpus
compatibilities, SpringerPlus 2(1) (2013) 601.
34. A. Ekbal and S. Saha, Weighted vote-based classifier ensemble for named entity recognition: a
genetic algorithm-based approach, ACM Trans. Asian Lang. Inf. Process. 10(2) (2011) 1–37.
35. A. Ekbal and S. Saha, A multiobjective simulated annealing approach for classifier ensemble:
named entity recognition in indian languages as case studies, Expert Syst. Appl. 38(12) (2011)
14760–14772.
36. S. Saha and A. Ekbal, Combining multiple classifiers using vote based classifier ensemble technique for named entity recognition, Data Knowl. Eng. 85 (2013) 15–39.
37. S. Yadav, A. Ekbal and S. Saha, Feature selection for entity extraction from multiple biomedical
corpora: A PSO-based approach, Soft Comput. 22(20) (2018) 6881–6904.
38. S. Yadav, A. Ekbal and S. Saha, Information theoretic-PSO-based feature selection: an application
in biomedical entity extraction, Knowl. Inf. Syst. 60(3) (2019) 1453–1478.
39. L. Sun, F. Ji, K. Zhang and C. Wang, Multilayer ToI detection approach for nested NER, IEEE
Access 7 (2019) 186600–186608.
40. L. Sun, K. Zhang, Y. Sun, F. Weng and J. Zhang, ETIP: a lengthy nested NER problem for Chinese
insurance policy analysis, Pattern Anal. Appl. 23(4) (2020) 1755–1765.
41. L. Sun, Y. Sun, F. Ji and C. Wang, Joint learning of token context and span feature for span-based
nested NER, IEEEACM Trans. Audio Speech Lang. Process. 28 (2020) 2720–2730.
42. A. Akkasi, Sentence-based undersampling for named entity recognition using genetic algorithm,
Iran J. Comput. Sci. 1(3) (2018) 165–174.
43. H. Cho and H. Lee, Biomedical named entity recognition using deep neural networks with contextual information, BMC Bioinformatics 20(1) (2019) 735.
44. H. Cho, W. Choi and H. Lee, A method for named entity normalization in biomedical articles:
application to diseases and plants, BMC Bioinformatics 18(1) (2017) 451.
45. Y. Gao, L. Gu, Y. Wang, Y. Wang and F. Yang, Constructing a Chinese electronic medical record
corpus for named entity recognition on resident admit notes, BMC Med. Inform. Decis. Mak.
19(S2) (2019) 56.
46. Y. Gao, Y. Wang, P. Wang and L. Gu, Medical named entity extraction from Chinese resident
admit notes Using Character and Word Attention-Enhanced Neural Network, Int. J. Environ.
Res. Public. Health 17(5) (2020) 1614.
47. Q. Qiu, Z. Xie, L. Wu and L. Tao, GNER: a generative model for geological named entity recognition without labeled data using deep learning, Earth Space Sci. 6(6) (2019) 931–946.
48. Q. Qiu, Z. Xie, L. Wu, L. Tao and W. Li, BiLSTM-CRF for geological named entity recognition
from the geoscience literature, Earth Sci. Inform. 12(4) (2019) 565–579.
49. Z. Tang, L. Jiang, L. Yang, K. Li and K. Li, CRFs based parallel biomedical named entity recognition algorithm employing MapReduce framework, Clust. Comput. 18(2) (2015) 493–505.
50. Z. Tang, B. Wan and L. Yang, Word-character graph convolution network for Chinese named
entity recognition, IEEEACM Trans. Audio Speech Lang. Process. 28 (2020) 1520–1532.
51. N. Zhang, F. Li, G. Xu, W. Zhang and H. Yu, Chinese NER using dynamic meta-embeddings,
IEEE Access 7 (2019) 64450–64459.
52. N. Zhang, G. Xu, Z. Zhang and F. Li, MIFM: multi-granularity information fusion model for
Chinese named entity recognition, IEEE Access 7 (2019) 181648–181655.
53. G. Lample, M. Ballesteros, S. Subramanian, K. Kawakami and C. Dyer, Neural architectures for
named entity recognition, in Proc. 2016 Conf. North Am. Chapter Assoc. Comput. Linguist.
Hum. Lang. Technol. (Association for Computational Linguistics, San Diego, California, 2016),
pp. 260–270.
54. M. Ballesteros, C. Dyer and N. A. Smith, Improved transition-based parsing by modeling characters instead of words with LSTMs, ArXiv150800657 Cs (2015).
55. A. Ritter, S. Clark, Mausam and O. Etzioni, Named entity recognition in tweets: an experimental
study, in Proc. 2011 Conf. Empir. Methods Nat. Lang. Process. (Association for Computational
Linguistics, Edinburgh, Scotland, UK., 2011), pp. 1524–1534.
56. T. Eftimov, B. Koroušic Seljak and P. Korošec, A rule-based named-entity recognition method ´
for knowledge extraction of evidence-based dietary recommendations, PLOS ONE 12(6) (2017)
e0179488.
57. M. Dias, J. Boné, J. C. Ferreira, R. Ribeiro and R. Maia, Named entity recognition for sensitive
data discovery in Portuguese, Appl. Sci. 10(7) (2020) 2303.
58. W. Hemati and A. Mehler, LSTMVoter: chemical named entity recognition using a conglomerate
of sequence labeling tools, J. Cheminformatics 11(1) (2019) 3.
59. J. Atkinson and V. Bull, A multi-strategy approach to biological named entity recognition, Expert
Syst. Appl. 39(17) (2012) 12968–12974.
60. B. Bhasuran, G. Murugesan, S. Abdulkadhar and J. Natarajan, Stacked ensemble combined with
fuzzy matching for biomedical named entity recognition of diseases, J. Biomed. Inform. 64
(2016) 1–9.
61. C. Cabot, S. Darmoni and L. F. Soualmia, Cimind: a phonetic-based tool for multilingual named
entity recognition in biomedical texts, J. Biomed. Inform. 94 (2019) 103176.
62. L. Gligic, A. Kormilitzin, P. Goldberg and A. Nevado-Holgado, Named entity recognition in
electronic health records using transfer learning bootstrapped Neural Networks, Neural Netw.
121 (2020) 132–139.
63. Y. Zhang, O. Zhang, Y. Wu, H.-J. Lee, J. Xu, H. Xu and K. Roberts, Psychiatric symptom recognition without labeled data using distributional representations of phrases and on-line knowledge,
J. Biomed. Inform. 75 (2017) S129–S137.
64. M. Khalifa and K. Shaalan, Character convolutions for Arabic named entity recognition with long
short-term memory networks, Comput. Speech Lang. 58 (2019) 335–346.
65. M. Xiaofeng, W. Wei and X. Aiping, Incorporating token-level dictionary feature into neural
model for named entity recognition, Neurocomputing 375 (2020) 43–50.
66. C. C. Aggarwal, Machine Learning for Text (Springer International Publishing, New York, 2018).
67. M. Khabsa and C. L. Giles, Chemical entity extraction using CRF and an ensemble of extractors,
J. Cheminformatics 7(S1) (2015) S12.
68. A. Akkasi, E. Varoglu and N. Dimililer, ChemTok: a new rule based tokenizer for chemical named ˘
entity recognition, BioMed Res. Int. 2016 (2016) 1–9.
69. X. Cai, S. Dong and J. Hu, A deep learning model incorporating part of speech and self-matching
attention for named entity recognition of Chinese electronic medical records, BMC Med. Inform.
Decis. Mak. 19(S2) (2019) 65.
70. H.-C. Cho, N. Okazaki, M. Miwa and J. Tsujii, Named entity recognition with multiple segment
representations, Inf. Process. Manag. 49(4) (2013) 954–965.
71. V. Suárez-Paniagua, R. M. Rivera Zavala, I. Segura-Bedmar and P. Martínez, A two-stage deep
learning approach for extracting entities and relationships from medical texts, J. Biomed. Inform.
99 (2019) 103285.
72. B. Gaur, G. S. Saluja, H. B. Sivakumar and S. Singh, Semi-supervised deep learning based named
entity recognition model to parse education section of resumes, Neural Comput. Appl. 33(11)
(2020) 5705–5718.
73. C. Zhou, B. Li and X. Sun, Improving software bug-specific named entity recognition with deep
neural network, J. Syst. Softw. 165 (2020) 110572.
74. R. Batista-Navarro, R. Rak and S. Ananiadou, Optimising chemical named entity recognition
with pre-processing analytics, knowledge-rich features and heuristics, J. Cheminformatics 7(S1)
(2015) S6.
75. S. Bordag, C. Hänig and C. Beutenmüller, A structuralist approach for personal knowledge exploration systems on mobile devices, in Text Mining, eds. C. Biemann and A. Mehler (Springer
International Publishing, Cham, 2014), pp. 115–136.
76. H. Khalil, T. Osman and M. Miltan, Extracting Arabic composite names using genitive principles
of Arabic grammar, ACM Trans. Asian Low-Resour. Lang. Inf. Process. 19(4) (2020) 1–16.
77. Y.-Y. Hsu and H.-Y. Kao, Curatable named-entity recognition using semantic relations, IEEE/ACM
Trans. Comput. Biol. Bioinform. 12(4) (2015) 785–792.
78. L. Li, J. Zhao, L. Hou, Y. Zhai, J. Shi and F. Cui, An attention-based deep learning model for
clinical named entity recognition of Chinese electronic medical records, BMC Med. Inform.
Decis. Mak. 19(S5) (2019) 235.
79. G. Murugesan, S. Abdulkadhar, B. Bhasuran and J. Natarajan, BCC-NER: bidirectional, contextual clues named entity tagger for gene/protein mention recognition, EURASIP J. Bioinforma.
Syst. Biol. 2017(1) (2017) 7.
80. S. Song, N. Zhang and H. Huang, Named entity recognition based on conditional random fields,
Clust. Comput. 22(S3) (2019) 5195–5206.
81. R. Sivashankari and B. Valarmathi, NLP-MTFLR: document-level prioritization and identification of dominant multi-word named products in customer reviews, Arab. J. Sci. Eng. 43(2) (2018)
843–855.
82. Q. Zhao, D. Wang, J. Li and F. Akhtar, Exploiting the concept level feature for enhanced name
entity recognition in Chinese EMRs, J. Supercomput. 76(8) (2019) 6399–6420.
83. W. Huang, D. Hu, Z. Deng and J. Nie, Named entity recognition for Chinese judgment documents
based on BiLSTM and CRF, EURASIP J. Image Video Process. 2020(1) (2020) 52.
84. J. Wang, C. Lin, M. Li and C. Zaniolo, Boosting approximate dictionary-based entity extraction
with synonyms, Inf. Sci. 530 (2020) 1–21.
85. L. Li and Y. Jiang, Integrating language model and reading control gate in BLSTM-CRF for
biomedical named entity recognition, IEEE/ACM Trans. Comput. Biol. Bioinform. 17(3) (2018)
841–846.
86. Y. Wang, Y. Li, Z. Zhu, H. Tong and Y. Huang, Adversarial learning for multi-task sequence
labeling with attention mechanism, IEEEACM Trans. Audio Speech Lang. Process. 28 (2020)
1–1.
87. X. Zhong, E. Cambria and A. Hussain, Extracting time expressions and named entities with
constituent-based tagging schemes, Cogn. Comput. 12(4) (2020) 844–862.
88. X. Liu, N. Yang, Y. Jiang, L. Gu and X. Shi, A parallel computing-based deep attention model
for named entity recognition, J. Supercomput. 76(2) (2020) 814–830.
89. H.-S. Chang, S. Vembu, S. Mohan, R. Uppaal and A. McCallum, Using error decay prediction
to overcome practical issues of deep active learning for named entity recognition, Mach. Learn.
109(9-10) (2020) 1749–1778.
90. E. F. T. K. Sang and F. De Meulder, Introduction to the CoNLL-2003 shared task: languageindependent named entity recognition, ArXiv:Cs/0306050 (2003).
91. J. Wang, W. Xu, X. Fu, G. Xu and Y. Wu, ASTRAL: Adversarial trained LSTM-CNN for named
entity recognition, Knowl.-Based Syst. 197 (2020) 105842.
92. J. P. C. Chiu and E. Nichols, Named entity recognition with bidirectional LSTM-CNNs, Trans.
Assoc. Comput. Linguist. 4 (2016) 357–370.
93. G. Aguilar, F. AlGhamdi, V. Soto, M. Diab, J. Hirschberg and T. Solorio, Named entity recognition on code-switched data: overview of the calcs 2018 shared task, in Proc. Third Workshop
Comput. Approaches Linguist. Code-Switch. (Association for Computational Linguistics, Melbourne, Australia, 2018), pp. 138–147.
94. M. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee and L. Zettlemoyer, deep contextualized word representations, in Proc. 2018 Conf. North Am. Chapter Assoc. Comput. Linguist.
Hum. Lang. Technol. Vol. 1 Long Pap. (Association for Computational Linguistics, New Orleans,
Louisiana, 2018), pp. 2227–2237.
95. K. Clark, M.-T. Luong, C. D. Manning and Q. V. Le, Semi-supervised sequence modeling with
cross-view training, ArXiv Prepr. ArXiv180908370 (2018).
96. J. Devlin, M.-W. Chang, K. Lee and K. Toutanova, BERT: Pre-training of deep bidirectional
transformers for language understanding, ArXiv Prepr. ArXiv181004805 (2019).
97. Akbik, D. Blythe and R. Vollgraf, Contextual string embeddings for sequence labeling, in Proc.
27th Int. Conf. Comput. Linguist. (Association for Computational Linguistics, Santa Fe, New
Mexico, USA, 2018), pp. 1638–1649.
98. S. Bratus, A. Rumshisky, A. Khrabrov, R. Magar and P. Thompson, Domain-specific entity extraction from noisy, unstructured data using ontology-guided search, Int. J. Doc. Anal. Recognit.
IJDAR 14(2) (2011) 201–211.
99. X. Guo, H. Zhou, J. Su, X. Hao, Z. Tang, L. Diao and L. Li, Chinese agricultural diseases and pests
named entity recognition with multi-scale local context features and self-attention mechanism,
Comput. Electron. Agric. 179 (2020) 105830.
100. C. Liu, C. Fan, Z. Wang and Y. Sun, An instance transfer-based approach using enhanced recurrent neural network for domain named entity recognition, IEEE Access 8 (2020) 45263–45270.
101. J. Kim, Y. Ko and J. Seo, Construction of machine-labeled data for improving named entity
recognition by transfer learning, IEEE Access 8 (2020) 59684–59693.
102. J. Kim, Y. Ko and J. Seo, A bootstrapping approach with crf and deep learning models for improving the biomedical named entity recognition in multi-domains, IEEE Access 7 (2019) 70308–
70318.
103. X. Seti, A. Wumaier, T. Yibulayin, D. Paerhati, L. Wang and A. Saimaiti, Named-entity recognition in sports field based on a character-level graph convolutional network, Information 11(1)
(2020) 30.
104. F. Liao, L. Ma, J. Pei and L. Tan, Combined self-attention mechanism for Chinese named entity
recognition in military, Future Internet 11(8) (2019) 180.
105. S. Johnson, S. Shen and Y. Liu, CWPC_BiAtt: character–word–position combined bilstm-attention
for Chinese named entity recognition, Information 11(1) (2020) 45.
106. Y. Jin, J. Xie, W. Guo, C. Luo, D. Wu and R. Wang, LSTM-CRF neural network with gated self
attention for Chinese NER, IEEE Access 7 (2019) 136694–136703.
107. A. Ekbal and S. Saha, Multiobjective optimization for classifier ensemble and feature selection:
an application to named entity recognition, Int. J. Doc. Anal. Recognit. IJDAR 15(2) (2012)
143–166.
108. J. Xu, H. He, X. Sun, X. Ren and S. Li, Cross-domain and semisupervised named entity recognition in Chinese social media: a unified model, IEEEACM Trans. Audio Speech Lang. Process.
26(11) (2018) 2142–2152.
109. Y. Song, S. Jeong and H. Kim, Semi-automatic construction of a named entity dictionary for
entity-based sentiment analysis in social media, Multimed. Tools Appl. 76(9) (2017) 11319–
11329.
110. M. Song, W. C. Kim, D. Lee, G. E. Heo and K. Y. Kang, PKDE4J: entity and relation extraction
for public knowledge discovery, J. Biomed. Inform. 57 (2015) 320–332.
111. G. Crichton, S. Pyysalo, B. Chiu and A. Korhonen, A neural network multi-task learning approach
to biomedical named entity recognition, BMC Bioinformatics 18(1) (2017) 368.
112. X. Dong, S. Chowdhury, L. Qian, X. Li, Y. Guan, J. Yang and Q. Yu, deep learning for named
entity recognition on chinese electronic medical records: combining deep transfer learning with
multitask bi-directional LSTM RNN, PLOS ONE 14(5) (2019) e0216046.
113. X. Li, C. Fu, R. Zhong, D. Zhong, T. He and X. Jiang, A hybrid deep learning framework for
bacterial named entity recognition with domain features, BMC Bioinformatics 20(S16) (2019)
583.
114. M. Gao, Q. Xiao, S. Wu and K. Deng, An improved method for named entity recognition and its
application to CEMR, Future Internet 11(9) (2019) 185.
115. E. Batbaatar and K. H. Ryu, Ontology-based healthcare named entity recognition from twitter
messages using a recurrent neural network approach, Int. J. Environ. Res. Public. Health 16(19)
(2019) 3628.
116. X. Chen, C. Ouyang, Y. Liu and Y. Bu, Improving the named entity recognition of Chinese electronic medical records by combining domain dictionary and rules, Int. J. Environ. Res. Public.
Health 17(8) (2020) 2687.
117. Y. Wang, S. Ananiadou and J. Tsujii, Improving clinical named entity recognition in Chinese
using the graphical and phonetic feature, BMC Med. Inform. Decis. Mak. 19(S7) (2019) 273.
118. K. Xu, Z. Zhou, T. Gong, T. Hao and W. Liu, SBLC: a hybrid model for disease named entity
recognition based on semantic bidirectional LSTMs and conditional random fields, BMC Med.
Inform. Decis. Mak. 18(S5) (2018) 114.
119. C. Lyu, B. Chen, Y. Ren and D. Ji, Long short-term memory RNN for biomedical named entity
recognition, BMC Bioinformatics 18(1) (2017) 462.
120. B. Ji, R. Liu, S. Li, J. Yu, Q. Wu, Y. Tan and J. Wu, A hybrid approach for named entity recognition in Chinese electronic medical record, BMC Med. Inform. Decis. Mak. 19(S2) (2019)
64.
121. S. Chowdhury, X. Dong, L. Qian, X. Li, Y. Guan, J. Yang and Q. Yu, A multitask bi-directional
RNN model for named entity recognition on Chinese electronic medical records, BMC Bioinformatics 19(S17) (2018) 499.
122. R. Patra and S. K. Saha, A Kernel-based approach for biomedical named entity recognition, Sci.
World J. 2013 (2013) 1-7.
123. W. Yoon, C. H. So, J. Lee and J. Kang, CollaboNet: collaboration of deep neural networks for
biomedical named entity recognition, BMC Bioinformatics 20(S10) (2019) 249.
124. Z. Zhao, Z. Yang, L. Luo, L. Wang, Y. Zhang, H. Lin and J. Wang, Disease named entity recognition from biomedical literature using a novel convolutional neural network, BMC Med. Genomics
10(S5) (2017) 73.
125. Y. Lou, T. Qian, F. Li and D. Ji, A graph attention model for dictionary-guided named entity
recognition, IEEE Access 8 (2020) 71584–71592.
126. J. Qiu, Y. Zhou, Q. Wang, T. Ruan and J. Gao, Chinese clinical named entity recognition using residual dilated convolutional neural network with conditional random field, IEEE Trans.
NanoBioscience 18(3) (2019) 306–315.
127. K. Li, W. Ai, Z. Tang, F. Zhang, L. Jiang, K. Li and K. Hwang, Hadoop recognition of biomedical
named entity using conditional random fields, IEEE Trans. Parallel Distrib. Syst. 26(11) (2015)
3040–3051.
128. G. Wu, G. Tang, Z. Wang, Z. Zhang and Z. Wang, An attention-based BiLSTM-CRF model for
Chinese clinic named entity recognition, IEEE Access 7 (2019) 113942–113949.
129. D. Kim, J. Lee, C. H. So, H. Jeon, M. Jeong, Y. Choi, W. Yoon, M. Sung and J. Kang, A neural
named entity recognition and multi-type normalization tool for biomedical text mining, IEEE
Access 7 (2019) 73729–73740.
130. H. Wei, M. Gao, A. Zhou, F. Chen, W. Qu, C. Wang and M. Lu, Named entity recognition from
biomedical texts using a fusion attention-based BiLSTM-CRF, IEEE Access 7 (2019) 73627–
73636.
131. A. Casillas, N. Ezeiza, I. Goenaga, A. Pérez and X. Soto, Measuring the effect of different types
of unsupervised word representations on Medical Named Entity Recognition, Int. J. Med. Inf.
129 (2019) 100–106.
132. J. Chae, Y. Jung, T. Lee, S. Jung, C. Huh, G. Kim, H. Kim and H. Oh, Identifying non-elliptical
entity mentions in a coordinated NP with ellipses, J. Biomed. Inform. 47 (2014) 139–152.
133. Y. Chen, C. Zhou, T. Li, H. Wu, X. Zhao, K. Ye and J. Liao, Named entity recognition from
Chinese adverse drug event reports with lexical feature based BiLSTM-CRF and tri-training, J.
Biomed. Inform. 96 (2019) 103252.
134. M. Cho, J. Ha, C. Park and S. Park, Combinatorial feature embedding based on CNN and LSTM
for biomedical named entity recognition, J. Biomed. Inform. 103 (2020) 103381.
135. M. Gridach, Character-level neural network for biomedical named entity recognition, J. Biomed.
Inform. 70 (2017) 85–91.
136. J. Guo, C. Blake and Y. Guan, Evaluating automated entity extraction with respect to drug and
non-drug treatment strategies, J. Biomed. Inform. 94 (2019) 103177.
137. I. J. Unanue, E. Z. Borzeshi and M. Piccardi, Recurrent neural networks with specialized word
embeddings for health-domain named-entity recognition, J. Biomed. Inform. 76 (2017) 102–109.
138. S. Santiso, A. Pérez, A. Casillas and M. Oronoz, Neural negated entity recognition in Spanish
electronic health records, J. Biomed. Inform. 105 (2020) 103419.
139. J. Urbain, Mining heart disease risk factors in clinical text with named entity recognition and
distributional semantic models, J. Biomed. Inform. 58 (2015) S143–S149.
140. Q. Wang, Y. Zhou, T. Ruan, D. Gao, Y. Xia and P. He, Incorporating dictionaries into deep neural
networks for the Chinese clinical named entity recognition, J. Biomed. Inform. 92 (2019) 103133.
141. M. Yin, C. Mou, K. Xiong and J. Ren, Chinese clinical named entity recognition with radical-level
feature and self-attention mechanism, J. Biomed. Inform. 98 (2019) 103289.
142. S. Zhang and N. Elhadad, Unsupervised biomedical named entity recognition: Experiments with
clinical and biological texts, J. Biomed. Inform. 46(6) (2013) 1088–1098.
143. S. Zhao, Z. Cai, H. Chen, Y. Wang, F. Liu and A. Liu, Adversarial training based lattice LSTM
for Chinese clinical named entity recognition, J. Biomed. Inform. 99 (2019) 103290.
144. A. Pérez, R. Weegar, A. Casillas, K. Gojenola, M. Oronoz and H. Dalianis, Semi-supervised
medical entity recognition: A study on Spanish and Swedish clinical corpora, J. Biomed. Inform.
71 (2017) 16–30.
145. A. Akkasi, E. Varoglu and N. Dimililer, Balanced undersampling: a novel sentence-based under- ˘
sampling method to improve recognition of named entities in chemical and biomedical text, Appl.
Intell. 48(8) (2018) 1965–1978.
146. M. Basaldella, L. Furrer, C. Tasso and F. Rinaldi, Entity recognition in the biomedical domain
using a hybrid approach, J. Biomed. Semant. 8(1) (2017) 51.
147. Y. Qin and Y. Zeng, Research of clinical named entity recognition based on Bi-LSTM-CRF, J.
Shanghai Jiaotong Univ. Sci. 23(3) (2018) 392–397.
148. B. Tang, X. Wang, J. Yan and Q. Chen, Entity recognition in Chinese clinical text using attentionbased CNN-LSTM-CRF, BMC Med. Inform. Decis. Mak. 19(S3) (2019) 74.
149. R. Weegar, A. Pérez, A. Casillas and M. Oronoz, Recent advances in Swedish and Spanish medical entity recognition in clinical texts using deep neural approaches, BMC Med. Inform. Decis.
Mak. 19(S7) (2019) 274.
150. L. Yang and Y. Zhou, Exploring feature sets for two-phase biomedical named entity recognition
using semi-CRFs, Knowl. Inf. Syst. 40(2) (2014) 439–453.
151. L. Yao, C. Sun, Y. Wu, X. Wang and X. Wang, Biomedical named entity recognition using generalized expectation criteria, Int. J. Mach. Learn. Cybern. 2(4) (2011) 235–243.
152. S. M. Yimam, C. Biemann, L. Majnaric, Š. Šabanovic and A. Holzinger, An adaptive annotation ´
approach for biomedical entity and relation recognition, Brain Inform. 3(3) (2016) 157–168.
153. M. Liu, Y. Zhang, W. Li and D. Ji, Joint model of entity recognition and relation extraction with
self-attention mechanism, ACM Trans. Asian Low-Resour. Lang. Inf. Process. 19(4) (2020)
1–19.
154. R. Catelli, F. Gargiulo, V. Casola, G. De Pietro, H. Fujita and M. Esposito, Crosslingual named
entity recognition for clinical de-identification applied to a COVID-19 Italian data set, Appl. Soft
Comput. 97 (2020) 106779.
155. Y. Song, S. Tian and L. Yu, A method for identifying local drug names in Xinjiang based on
BERT-BiLSTM-CRF, Autom. Control Comput. Sci. 54(3) (2020) 179–190.
156. Y. Tian, W. Shen, Y. Song, F. Xia, M. He and K. Li, Improving biomedical named entity recognition with syntactic information, BMC Bioinformatics 21(1) (2020) 539.
157. Y.-M. Kim and T.-H. Lee, Korean clinical entity recognition from diagnosis text using BERT,
BMC Med. Inform. Decis. Mak. 20(S7) (2020) 242.
158. L. Akhtyamova, P. Martinez, K. Verspoor and J. Cardiff, Testing contextualized word embeddings
to improve NER in Spanish clinical case narratives, IEEE Access 8 (2020) 164717–164726.
159. D. Zhang, C. Xia, C. Xu, Q. Jia, S. Yang, X. Luo and Y. Xie, Improving distantly-supervised
named entity recognition for traditional Chinese medicine text via a novel back-labeling approach,
IEEE Access 8 (2020) 145413–145421.
160. G. Wen, H. Chen, H. Li, Y. Hu, Y. Li and C. Wang, Cross domains adversarial learning for Chinese
named entity recognition for online medical consultation, J. Biomed. Inform. 112 (2020) 103608.
161. C. Wang, H. Wang, H. Zhuang, W. Li, S. Han, H. Zhang and L. Zhuang, Chinese medical named
entity recognition based on multi-granularity semantic dictionary and multimodal tree, J. Biomed.
Inform. 111 (2020) 103583.
162. C. Wu, G. Luo, C. Guo, Y. Ren, A. Zheng and C. Yang, An attention-based multi-task model
for named entity recognition and intent analysis of Chinese online medical questions, J. Biomed.
Inform. 108 (2020) 103511.
163. B. Ji, S. Li, J. Yu, J. Ma, J. Tang, Q. Wu, Y. Tan, H. Liu and Y. Ji, Research on Chinese medical
named entity recognition based on collaborative cooperation of multiple neural network models,
J. Biomed. Inform. 104 (2020) 103395.
164. I. Lerner, N. Paris and X. Tannier, Terminologies augmented recurrent neural network model for
clinical named entity recognition, J. Biomed. Inform. 102 (2020) 103356.
165. T. Iwakura, H. Takamura and M. Okumura, A named entity recognition method based on decomposition and concatenation of word chunks, ACM Trans. Asian Lang. Inf. Process. 12(3) (2013)
1–18.
166. D. Deng, G. Li, J. Feng, Y. Duan and Z. Gong, A unified framework for approximate dictionarybased entity extraction, VLDB J. 24(1) (2015) 143–167.
167. R. Murthy, M. M. Khapra and P. Bhattacharyya, Improving NER tagging performance in lowresource languages via multilingual learning, ACM Trans. Asian Low-Resour. Lang. Inf. Process.
18(2) (2019) 1–20.
168. L. Li, P. Wang, D. Huang and L. Zhao, Mining English-Chinese named entity pairs from comparable corpora, ACM Trans. Asian Lang. Inf. Process. 10(4) (2011) 1–19.
169. L. Zhao, X. Qiu, Q. Zhang and X. Huang, Sequence labeling with deep gated dual path CNN,
IEEEACM Trans. Audio Speech Lang. Process. 27(12) (2019) 2326–2335.
170. H. Kim and H. Kim, Integrated model for morphological analysis and named entity recognition
based on label attention networks in Korean, Appl. Sci. 10(11) (2020) 3740.
171. W. Khan, A. Daud, F. Alotaibi, N. Aljohani and S. Arafat, Deep recurrent neural networks with
word embeddings for Urdu named entity recognition, ETRI J. 42(1) (2020) 90–100.
172. M. Ali, G. Tan and A. Hussain, Bidirectional recurrent neural network approach for Arabic named
entity recognition, Future Internet 10(12) (2018) 123.
173. A. Saimaiti, L. Wang and T. Yibulayin, Learning subword embedding to improve Uyghur namedentity recognition, Information 10(4) (2019) 139.
174. A. P. Ajees, K. Manju and S. Mary Idicula, An improved word representation for deep learning
based NER in Indian languages, Information 10(6) (2019) 186.
175. B. Peng, J. Wu, H. Yuan, Q. Guo and D. Tao, ANEEC: A quasi-automatic system for massive
named entity extraction and categorization, Comput. J. 56(11) (2013) 1328–1346.
176. H. Zhu, C. He, Y. Fang and W. Xiao, Fine grained named entity recognition via Seq2seq framework, IEEE Access 8 (2020) 53953–53961.
177. M. Al-Smadi, S. Al-Zboon, Y. Jararweh and P. Juola, Transfer learning for Arabic named entity
recognition with deep neural networks, IEEE Access 8 (2020) 37736–37745.
178. I. Ashrafi, M. Mohammad, A. S. Mauree, G. Md. A. Nijhum, R. Karim, N. Mohammed and
S. Momen, Banner: A cost-sensitive contextualized model for Bangla named entity recognition,
IEEE Access 8 (2020) 58206–58226.
179. D. Li, X. Zhang and X. Wu, Integrated Chinese segmentation, parsing and named entity recognition, Chin. J. Electron. 27(4) (2018) 756–760.
180. M. N. A. Ali, G. Tan and A. Hussain, Boosting Arabic named-entity recognition with multiattention layer, IEEE Access 7 (2019) 46575–46582.
181. R. Agerri and G. Rigau, Robust multilingual named entity recognition with shallow semi-supervised
features, Artif. Intell. 238 (2016) 63–82.
182. G. Bekoulis, J. Deleu, T. Demeester and C. Develder, Joint entity recognition and relation extraction as a multi-head selection problem, Expert Syst. Appl. 114 (2018) 34–45.
183. E. Fersini, E. Messina, G. Felici and D. Roth, Soft-constrained inference for named entity recognition, Inf. Process. Manag. 50(5) (2014) 807–819.
184. O. Ghiasvand and R. J. Kate, Learning for clinical named entity recognition without manual
annotations, Inform. Med. Unlocked 13 (2018) 122–127.
185. S.-H. Na, H. Kim, J. Min and K. Kim, Improving LSTM CRFs using character-based compositions for Korean named entity recognition, Comput. Speech Lang. 54 (2019) 106–121.
186. M. Konkol, T. Brychcín and M. Konopík, Latent semantics in named entity recognition, Expert
Syst. Appl. 42(7) (2015) 3470–3479.
187. M. N. A. Ali and G. Tan, Bidirectional encoder–decoder model for Arabic named entity recognition, Arab. J. Sci. Eng. 44(11) (2019) 9693–9701.
188. H. Al-Jumaily, P. Martínez, J. L. Martínez-Fernández and E. Van der Goot, A real time named
entity recognition system for Arabic text mining, Lang. Resour. Eval. 46(4) (2012) 543–563.
189. K.-Y. Cui, P.-J. Ren, Z.-M. Chen, T. Lian and J. Ma, Relation enhanced neural model for type
classification of entity mentions with a fine-grained taxonomy, J. Comput. Sci. Technol. 32(4)
(2017) 814–827.
190. C. de Pablo-Sánchez, I. Segura-Bedmar, P. Martínez and A. Iglesias-Maqueda, Lightly supervised
acquisition of named entities and linguistic patterns for multilingual text mining, Knowl. Inf. Syst.
35(1) (2013) 87–109.
191. B. Desmet and V. Hoste, Fine-grained Dutch named entity recognition, Lang. Resour. Eval. 48(2)
(2014) 307–343.
192. Z. Li, D. Feng, D. Li and X. Lu, Learning to select pseudo labels: a semi-supervised method for
named entity recognition, Front. Inf. Technol. Electron. Eng. 21(6) (2020) 903–916.
193. R. Lima, B. Espinasse and F. Freitas, OntoILPER: an ontology- and inductive logic programmingbased system to extract entities and relations from text, Knowl. Inf. Syst. 56(1) (2018) 223–255.
194. H. Moradi, F. Ahmadi and M.-R. Feizi-Derakhshi, A hybrid approach for Persian named entity
recognition, Iran. J. Sci. Technol. Trans. Sci. 41(1) (2017) 215–222.
195. D. Peng, Y. Wang, C. Liu and Z. Chen, TL-NER: a transfer learning model for Chinese named
entity recognition, Inf. Syst. Front. 22(6) (2020) 1291–1304.
196. R. Sharma, S. Morwal, B. Agarwal, R. Chandra and M. S. Khan, A deep neural network-based
model for named entity recognition for Hindi language, Neural Comput. Appl. 32(20) (2020)
16191–16203.
197. M. Song, H. Yu and W.-S. Han, Developing a hybrid dictionary-based bio-entity recognition
technique, BMC Med. Inform. Decis. Mak. 15(S1) (2015) S9.
198. Z. Wang, X. Cui, L. Gao, Q. Yin, L. Ke and S. Zhang, A hybrid model of sentimental entity
recognition on mobile social media, EURASIP J. Wirel. Commun. Netw. 2016(1) (2016) 253.
199. C.-L. Chou, C.-H. Chang, Y.-H. Lin and K.-C. Chien, On the construction of web NER model
training tool based on distant supervision, ACM Trans. Asian Low-Resour. Lang. Inf. Process.
19(6) (2020) 1–28.
200. M. S. H. Ameur, R. Belkebir and A. Guessoum, Robust Arabic text categorization by combining
convolutional and recurrent neural networks, ACM Trans. Asian Low-Resour. Lang. Inf. Process.
19(5) (2020) 1–16.
201. N. Alshammari and S. Alanazi, The impact of using different annotation schemes on named entity
recognition, Egypt. Inform. J. 22(3) (2021) 295–302.
202. J. T. Zhou, H. Zhang, D. Jin, X. Peng, Y. Xiao and Z. Cao, RoSeq: robust sequence labeling,
IEEE Trans. Neural Netw. Learn. Syst. (2019) 1–11.
203. C. Gong, Z. Li, Q. Xia, W. Chen and M. Zhang, Hierarchical LSTM with char-subword-word
tree-structure representation for Chinese named entity recognition, Sci. China Inf. Sci. 63(10)
(2020) 202102.
204. M. Li, T. Munkhdalai, X. Yu and K. H. Ryu, A novel approach for protein-named entity recognition and protein-protein interaction extraction, Math. Probl. Eng. 2015 (2015) 1–10.
205. F. Zhu and B. Shen, Combined SVM-CRFs for biological named entity recognition with maximal
bidirectional squeezing, PLoS ONE 7(6) (2012) e39230.
206. H. Zhou, S. Ning, Z. Liu, C. Lang, Z. Liu and B. Lei, Knowledge-enhanced biomedical named
entity recognition and normalization: application to proteins and genes, BMC Bioinformatics
21(1) (2020) 35.
207. R. Danger, F. Pla, A. Molina and P. Rosso, Towards a protein–protein interaction information
extraction system: recognizing named entities, Knowl.-Based Syst. 57 (2014) 104–118.
208. H. Fei, Y. Ren and D. Ji, Dispatched attention with multi-task learning for nested mention recognition, Inf. Sci. 513 (2020) 241–251.
209. A. Lourenço, M. Conover, A. Wong, A. Nematzadeh, F. Pan, H. Shatkay and L. M. Rocha, Correction: a linear classifier based on entity recognition tools and a statistical approach to method
extraction in the protein-protein interaction literature, BMC Bioinformatics 13(1) (2012) 180.
210. X. Wang, Y. Li, T. He, X. Jiang and X. Hu, Recognition of bacteria named entity using conditional
random fields in Spark, BMC Syst. Biol. 12(S6) (2018) 106.
211. H. Zhang, Y. Guo and T. Li, Multifeature named entity recognition in information security based
on adversarial learning, Secur. Commun. Netw. 2019 (2019) 1–9.
212. F. Yi, B. Jiang, L. Wang and J. Wu, Cybersecurity named entity recognition using multi-modal
ensemble learning, IEEE Access 8 (2020) 63214–63224.
213. M. W. Al-Nabki, E. Fidalgo, E. Alegre and L. Fernández-Robles, Improving named entity recognition in noisy user-generated text with local distance neighbor feature, Neurocomputing 382
(2020) 1–11.
214. G. Kim, C. Lee, J. Jo and H. Lim, Automatic extraction of named entities of cyber threats using a
deep Bi-LSTM-CRF network, Int. J. Mach. Learn. Cybern. 11(10) (2020) 2341–2355.
215. M. M. Tikhomirov, N. V. Loukachevitch and B. V. Dobrov, Recognizing named entities in specific
domain, Lobachevskii J. Math. 41(8) (2020) 1591–1602.
216. P. Ma, B. Jiang, Z. Lu, N. Li and Z. Jiang, Cybersecurity named entity recognition using bidirectional long short-term memory with conditional random fields, Tsinghua Sci. Technol. 26(3)
(2021) 259–265.
217. T. Munkhdalai, M. Li, K. Batsuren, H. A. Park, N. H. Choi and K. H. Ryu, Incorporating domain
knowledge in chemical and biomedical named entity recognition with word representations, J.
Cheminformatics 7(S1) (2015) S9.
218. Y. Chen, Y. Hu, Y. Li, R. Huang, Y. Qin, Y. Wu, Q. Zheng and P. Chen, A Boundary assembling
method for nested biomedical named entity recognition, IEEE Access 8 (2020) 214141–214152.
219. S. Dadas and J. Protasiewicz, A bidirectional iterative algorithm for nested named entity recognition, IEEE Access 8 (2020) 135091–135102.
220. S. Gajendran, D. Manjula and V. Sugumaran, Character level and word level embedding with
bidirectional LSTM – dynamic recurrent neural network for biomedical named entity recognition
from literature, J. Biomed. Inform. 112 (2020) 103609.
221. P. Corbett and J. Boyle, Chemlistem: chemical named entity recognition using recurrent neural
networks, J. Cheminformatics 10(1) (2018) 59.
222. B. Kolluru, L. Hawizy, P. Murray-Rust, J. Tsujii and S. Ananiadou, Using workflows to explore
and optimise named entity recognition for chemistry, PLoS ONE 6(5) (2011) e20181.
223. I. Korvigo, M. Holmatov, A. Zaikovskii and M. Skoblov, Putting hands to rest: efficient deep
CNN-RNN architecture for chemical named entity recognition with no hand-crafted rules, J.
Cheminformatics 10(1) (2018) 28.
224. L. Luo, Z. Yang, P. Yang, Y. Zhang, L. Wang, J. Wang and H. Lin, A neural network approach to
chemical and gene/protein entity recognition in patents, J. Cheminformatics 10(1) (2018) 65.
225. Y. Yao and A. Sun, Mobile phone name extraction from internet forums: a semi-supervised approach, World Wide Web 19(5) (2016) 783–805.
226. R. Fan, L. Wang, J. Yan, W. Song, Y. Zhu and X. Chen, Deep learning-based named entity
recognition and knowledge graph construction for geological hazards, ISPRS Int. J. Geo-Inf.
9(1) (2019) 15.
227. H. Huang, H. Wang and D. Jin, A low-cost named entity recognition research based on active
learning, Sci. Program. 2018 (2018) 1–10.
228. M. Zhang, G. Geng and J. Chen, Semi-supervised bidirectional long short-term memory and
conditional random fields model for named-entity recognition using embeddings from language
models representations, Entropy 22(2) (2020) 252.
229. C. Yan, Q. Su and J. Wang, MoGCN: Mixture of gated convolutional neural network for named
entity recognition of Chinese historical texts, IEEE Access 8 (2020) 181629–181639.
230. M. Shardlow, M. Ju, M. Li, C. O’Reilly, E. Iavarone, J. McNaught and S. Ananiadou, A text
mining pipeline using active and deep learning aimed at curating information in computational
neuroscience, Neuroinformatics 17(3) (2019) 391–406.
231. A. Rajkomar, J. Dean and I. Kohane, Machine learning in medicine, N. Engl. J. Med. 380(14)
(2019) 1347–1358.
232. Y. Wang, Z. Yu, L. Chen, Y. Chen, Y. Liu, X. Hu and Y. Jiang, Supervised methods for symptom
name recognition in free-text clinical records of traditional Chinese medicine: an empirical study,
J. Biomed. Inform. 47 (2014) 91–104.
233. T. H. Truong, M. H. Dao and D. Q. Nguyen, COVID-19 named entity recognition for Vietnamese,
in Proc. 2021 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol.
(Association for Computational Linguistics, Online, 2021), pp. 2146–2153.
234. N. Colic, L. Furrer and F. Rinaldi, Annotating the pandemic: named entity recognition and normalisation in COVID-19 literature, in Proc. 1st Workshop NLP COVID-19 Part 2 EMNLP 2020
(Association for Computational Linguistics, Online, 2020).
235. X. Wang, X. Song, B. Li, Y. Guan and J. Han, Comprehensive named entity recognition on
CORD-19 with distant or weak supervision, ArXiv200312218 Cs (2020).
236. O. Levy, Y. Goldberg and I. Dagan, Improving distributional similarity with lessons learned from
word embeddings, Trans. Assoc. Comput. Linguist. 3 (2015) 211–225.
237. G. Lev, B. Klein and L. Wolf, In defense of word embedding for generic text representation,
in Natural Language Processing and Information Systems, eds. C. Biemann, S. Handschuh, A.
Freitas, F. Meziane and E. Métais (Springer International Publishing, Cham, 2015), pp. 35–50.
238. M. Li, Q. Lu, Y. Long and L. Gui, Inferring affective meanings of words from word embedding,
IEEE Trans. Affect. Comput. 8 (2017) 443–456.
239. U. Kamath, J. Liu and J. Whitaker, Deep Learning for NLP and Speech Recognition (Springer
International Publishing, Cham, 2019).
240. F. M. Hasan, N. UzZaman and M. Khan, Comparison of different POS tagging techniques (ngram, HMM and Brill’s tagger) for Bangla, in Adv. Innov. Syst. Comput. Sci. Softw. Eng., ed. K.
Elleithy (Springer Netherlands, Dordrecht, 2007), pp. 121–126.
241. A. A. Farzindar and D. Inkpen, Natural language processing for social media, third edition, Synth.
Lect. Hum. Lang. Technol. 13(2) (2020) 1–219.
242. B. Y. Lin, F. Xu, Z. Luo and K. Zhu, Multi-channel BiLSTM-CRF model for emerging named
entity recognition in social media, in Proc. 3rd Workshop Noisy User-Gener. Text (Association
for Computational Linguistics, Copenhagen, Denmark, 2017), pp. 160–165.
243. X. Li, H. Zhang and X.-H. Zhou, Chinese clinical named entity recognition with variant neural
structures based on BERT methods, J. Biomed. Inform. 107 (2020) 103422.
244. C. Sun and Z. Yang, Transfer learning in biomedical named entity recognition: an evaluation of
BERT in the PharmaCoNER task, in Proc. 5th Workshop BioNLP Open Shar. Tasks (Association
for Computational Linguistics, Hong Kong, China, 2019), pp. 100–104.
245. J. Gao, M. Li, A. Wu and C.-N. Huang, Chinese word segmentation and named entity recognition:
a pragmatic approach, Comput. Linguist. 31(4) (2005) 531–574.
246. A. Krizhevsky, I. Sutskever and G. E. Hinton, ImageNet classification with deep convolutional
neural networks, Commun. ACM 60(6) (2017) 84–90.
247. S. Francis, J. V. Landeghem and M.-F. Moens, Transfer learning for named entity recognition in
financial and biomedical documents, Information 10(8) (2019) 248.
248. J. D. Lafferty, A. McCallum and F. C. N. Pereira, Conditional random fields: probabilistic models
for segmenting and labeling sequence data, in Proc. Eighteenth Int. Conf. Mach. Learn. (Morgan
Kaufmann Publishers Inc., San Francisco, CA, USA, 2001), pp. 282–289.
249. S. Ananiadou, D. Sullivan, W. Black, G.-A. Levow, J. J. Gillespie, C. Mao, S. Pyysalo, B. Kolluru,
J. Tsujii and B. Sobral, Named entity recognition for bacterial type IV secretion systems, PLoS
ONE 6(3) (2011) e14780.
250. Y. Tsuruoka, J. Tsujii and S. Ananiadou, Fast full parsing by linear-chain conditional random
fields, in Proc. 12th Conf. Eur. Chapter ACL EACL 2009 (Association for Computational Linguistics, Athens, Greece, 2009), pp. 790–798.
251. Y. Qin, G. Shen, W. Zhao, Y. Chen, M. Yu and X. Jin, A network security entity recognition
method based on feature template and CNN-BiLSTM-CRF, Front. Inf. Technol. Electron. Eng.
20(6) (2019) 872–884.
252. N. Greenberg, T. Bansal, P. Verga and A. McCallum, Marginal likelihood training of BiLSTMCRF for biomedical named entity recognition from disjoint label sets, in Proc. 2018 Conf. Empir.
Methods Nat. Lang. Process. (Association for Computational Linguistics, Brussels, Belgium,
2018), pp. 2824–2829.
253. S. Misawa, M. Taniguchi, Y. Miura and T. Ohkuma, Character-based bidirectional LSTM-CRF
with words and characters for Japanese Named entity recognition, in Proc. First Workshop Subword Character Level Models NLP (Association for Computational Linguistics, Copenhagen,
Denmark, 2017), pp. 97–102.
254. W. Wang, C. Xiao, X. Lin and C. Zhang, Efficient approximate entity extraction with edit distance
constraints, in Proc. 2009 ACM SIGMOD Int. Conf. Manag. Data (Association for Computing
Machinery, New York, NY, USA, 2009), pp. 759–770.
255. K. Chakrabarti, S. Chaudhuri, V. Ganti and D. Xin, An efficient filter for approximate membership
checking, in Proc. 2008 ACM SIGMOD Int. Conf. Manag. Data (Association for Computing
Machinery, New York, NY, USA, 2008), pp. 805–818.
256. D. T. Larose, Discovering Knowledge in Data: An Introduction to Data Mining (John Wiley &
Sons, Inc., Hoboken, NJ, USA, 2004).
257. M. Althobaiti, U. Kruschwitz and M. Poesio, Combining minimally-supervised methods for Arabic named entity recognition, Trans. Assoc. Comput. Linguist. 3 (2015) 243–255.
258. P. Cao, Y. Chen, K. Liu, J. Zhao and S. Liu, Adversarial transfer learning for Chinese named
entity recognition with self-attention mechanism, in Proc. 2018 Conf. Empir. Methods Nat.
Lang. Process. (Association for Computational Linguistics, Brussels, Belgium, 2018), pp. 182–
192.
259. M. Riedl and S. Padó, A named entity recognition shootout for German, in Proc. 56th Annu.
Meet. Assoc. Comput. Linguist. Vol. 2 Short Pap. (Association for Computational Linguistics,
Melbourne, Australia, 2018), pp. 120–125.
260. W. Lee, K. Kim, E. Y. Lee and J. Choi, Conditional random fields for clinical named entity
recognition: a comparative study using Korean clinical texts, Comput. Biol. Med. 101 (2018)
7–14.
261. D. C. Wintaka, M. A. Bijaksana and I. Asror, Named-entity recognition on Indonesian tweets
using bidirectional LSTM-CRF, Procedia Comput. Sci. 157 (2019) 221–228.
262. R. M. Syachrul M.A.K., M. A. Bijaksana and A. F. Huda, Person entity recognition for the Indonesian Qur’an translation with the approach hidden Markov Model-Viterbi, Procedia Comput.
Sci. 157 (2019) 214–220.
263. F. Y. Azalia, M. A. Bijaksana and A. F. Huda, Name Indexing in Indonesian translation of hadith
using named entity recognition with naïve Bayes classifier, Procedia Comput. Sci. 157 (2019)
142–149.
264. B. Wilie, K. Vincentio, G. I. Winata, S. Cahyawijaya, X. Li, Z. Y. Lim, S. Soleman, R. Mahendra, P. Fung, S. Bahar and A. Purwarianti, IndoNLU: benchmark and resources for evaluating
Indonesian natural language understanding, ArXiv200905387 Cs (2020).
265. A. S. Wibawa and A. Purwarianti, Indonesian named-entity recognition for 15 classes using ensemble supervised learning, Procedia Comput. Sci. 81 (2016) 221–228.
266. W. Gunawan, D. Suhartono, F. Purnomo and A. Ongko, Named-entity recognition for Indonesian
language using bidirectional LSTM-CNNs, Procedia Comput. Sci. 135 (2018) 425–432.
267. J. Xie, Z. Yang, G. Neubig, N. A. Smith and J. Carbonell, Neural cross-lingual named entity
recognition with minimal resources, ArXiv180809861 Cs (2018).
268. C. Sabty, M. Elmahdy and S. Abdennadher, Named entity recognition on Arabic-English codemixed data, in 2019 IEEE 13th Int. Conf. Semantic Comput. ICSC (IEEE, Newport Beach, CA,
USA, 2019), pp. 93–97.
269. A. Zafarian, A. Rokni, S. Khadivi and S. Ghiasifard, Semi-supervised learning for named entity
recognition using weakly labeled training data, in 2015 Int. Symp. Artif. Intell. Signal Process.
AISP (IEEE, Mashhad, Iran, 2015), pp. 129–135.
270. G. I. Winata, C.-S. Wu, A. Madotto and P. Fung, Bilingual character representation for efficiently
addressing out-of-vocabulary words in code-switching named entity recognition, in Proc. Third
Workshop Comput. Approaches Linguist. Code-Switch. (Association for Computational Linguistics, Melbourne, Australia, 2018), pp. 110–114.
271. L. H. B. Nguyen, D. Dinh and P. Tran, An approach to construct a named entity annotated EnglishVietnamese bilingual corpus, ACM Trans. Asian Low-Resour. Lang. Inf. Process. 16 (2016)
1–17.
272. T.-A. Dao, H.-T. Truong, L. Nguyen and D. Dinh, Building a named entity annotated bilingual
English-Vietnamese corpus, in 2018 10th Int. Conf. Knowl. Syst. Eng. KSE (IEEE, Ho Chi Minh
City, 2018), pp. 61–66.
273. M. Arkhipov, M. Trofimova, Y. Kuratov and A. Sorokin, Tuning multilingual transformers for
language-specific named entity recognition, in Proc. 7th Workshop Balto-Slav. Nat. Lang. Process. (Association for Computational Linguistics, Florence, Italy, 2019), pp. 89–93.
274. J. Ni and R. Florian, Improving multilingual named entity recognition with wikipedia entity type
mapping, in Proc. 2016 Conf. Empir. Methods Nat. Lang. Process. (Association for Computational Linguistics, Austin, Texas, 2016), pp 1275–1284.
Published
2024-02-28
How to Cite
Warto, W., Rustad, S., Shidik, G. F., Nursasongko, E., Purwanto, P., Muljono, M., & Setiadi, D. R. I. M. (2024). Systematic Literature Review on Named Entity Recognition: Approach, Method, and Application. Statistics, Optimization & Information Computing. https://doi.org/10.19139/soic-2310-5070-1631
Issue
Section
Research Articles
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
