• Journal of Terahertz Science and Electronic Information Technology
  • Vol. 22, Issue 5, 503 (2024)
MA Yijie1,*, LAI Haiguang2, LIU Ziwei1, YANG Nan1, and ZHANG Gengxin1
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • show less
    DOI: 10.11805/tkyda2023436 Cite this Article
    MA Yijie, LAI Haiguang, LIU Ziwei, YANG Nan, ZHANG Gengxin. Overview of the research progress in entity recognition technology[J]. Journal of Terahertz Science and Electronic Information Technology , 2024, 22(5): 503 Copy Citation Text show less
    References

    [1] LI Jing, SUN Aixin, HAN Jianglei, et al. A survey on deep learning for named entity recognition[J]. IEEE Transactions on Knowledge and Data Engineering, 2022,34( 1):50-70. doi:10. 1109/TKDE.2020.2981314.

    [2] GONZALEZ G L. A knowledge graph based integration approach for industry 4.0[D]. Nordrhein-Westfalen: Universit?t Bonn,2019.

    [3] LI Chenxi, HE Wenji,YAO Haipeng,et al. Knowledge graph aided network representation and routing algorithm for Leo satellite networks[J]. IEEE Transactions on Vehicular Technology, 2023,72(4):5195-5207. doi:10. 1109/TVT.2022.3225666.

    [4] KIM J H, WOODLAND P C. A rule-based named entity recognition system for speech input[C]// Proceedings of the 6th International Conference on Spoken Language Processing(ICSLP). Beijing, China: [s.n.], 2000: 528-531. doi: 10.21437/ICSLP.2000-131.

    [5] QUIMBAYA A P, SIERRA M A, GONZALEA RIVERA Q A. Named entity recognition over electronic health records through a combined dictionary-based approach[J]. Procedia Computer Science, 2016( 100):55-61.

    [6] HUANG Zhiheng,XU Wei,YU Kai. Bidirectional LSTM-CRF models for sequence tagging[DB/OL]. [2023-12-26]. https://arxiv. org/abs/ 1508.01991.

    [8] YI Feng, JIANG Bo, WANG Lu, et al. Cybersecurity named entity recognition using multi-modal ensemble learning[J]. IEEE Access, 2020(8):63214-63224. doi:10. 1109/ACCESS.2020.2984582.

    [10] LAFFERTY J D, MCCALLUM A, PEREIRA F C N. Conditional random fields:probabilistic models for segmenting and labeling sequence data[C]// Proceedings of the Eighteenth International Conference on Machine Learning. Williamstown, America: Morgan Kaufmann Publishers Inc, 2001:282-289. doi:10.5555/645530.655813.

    [11] SAMPATHKUMAR H, CHEN Xuewen, LUO Bo. Mining adverse drug reactions from online healthcare forums using hidden Markov model[J]. BMC Medical Informatics and Decision Making, 2014( 14):91. doi:10. 1186/ 1472-6947-14-91.

    [12] HANNA M W. Conditional random fields:an introduction:MS-CIS-04-21[R]. Philadelphia:University of Pennsylvania, 2004.

    [14] EMMA S,PATRICK V,DAVID B. Fast and accurate entity recognition with iterated dilated convolutions[C]// Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen, Denmark: ACL, 2017: 2670-2680. doi: 10. 18653/v 1/D17-1283.

    [15] LIU Wei,XU Tongge,XU Qinghua. An encoding strategy based word-character LSTM for Chinese NER[C]// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Minneapolis,Minnesota:ACL, 2019:2379-2389. doi:10. 18653/v 1/N19-1247.

    [16] XU Yongxiu, HUANG Heyang, FENG Chong, et al. A supervised multi-head self-attention network for nested named entity recognition[J]. AAAI Conference on Artificial Intelligence, 2021, 35( 16): 14185-14193. doi:https://doi.org/ 10. 1609/aaai.v35i 16. 17669.

    [18] YANG Zhiwei, MA Jing, CHEN Hechang. HiTRANS:a hierarchical transformer network for nested named entity recognition[C]// Proceedings of the Findings of the Association for Computational Linguistics. Punta Cana,Dominican Republic:ACL, 2021: 124-132. doi:10. 18653/v 1/2021.findings-emnlp. 12.

    [19] LAKKA E, PETROULAKIS N E, HATZIVASILIS G, et al. End-to-end semantic interoperability mechanisms for IoT [C]// 2019 IEEE 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks(CAMAD). Limassol,Cyprus:IEEE, 2019: 1-6. doi:10. 1109/CAMAD.2019.8858501.

    [20] RALPH G, BETH S. Message understanding conference-6: a brief history[C]// Proceedings of the 16th Conference on Computational Linguistics-Volume 1. Copenhagen Denmark:ACL, 1996:466-471. doi:10.3115/992628.992709.

    [24] ALI S, MASOOD K,RIAZ A,et al. Named entity recognition using deep learning:a review[C]// 2022 International Conference on Business Analytics for Technology and Security(ICBATS). Dubai,United Arab Emirates:IEEE, 2022: 1-7. doi:10. 1109/ICBATS5 4253.2022.9759051.

    [25] QIN Minghe, WANG Qinglin, LI Yuan. Research on entity mention recognition based on LSTM[C]// 2019 Chinese Control Conference(CCC). Guangzhou,China:IEEE, 2019:8712-8717. doi:10.23919/ChiCC.2019.8866207.

    [26] RAU L F. Extracting company names from text [C]// Proceedings the Seventh IEEE Conference on Artificial Intelligence Application. Miami Beach,FL,USA:IEEE, 1991:29-32. doi:10. 1109/CAIA. 1991. 120841.

    [27] SHAALAN K,RAZA H. NERA:Named Entity Recognition for Arabic[J]. Journal of the American Society for Information Science and Technology, 2009,60(8): 1652-1663. doi:10.5555/ 1572678. 1572692.

    [32] JANG H,SONG S K,MYAENG S H. Text mining for medical documents using a hidden Markov model[C]// Information Retrieval Technology. [S.l.]:Springer Berlin Heidelberg, 2006:553-559. doi:10. 1007/ 11880592_45.

    [33] LI Yong, MA Qixian, WANG Xia. Medical text entity recognition based on CRF and joint entity[C]// 2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers. Dalian, China: IEEE, 2021: 14-16. doi: 10. 1109/IPEC51340. 2021.9421264.

    [36] LI Lishuang, JIN Liuke, JIANG Zhenchao, et al. Biomedical named entity recognition based on extended Recurrent Neural Networks[C]// 2015 IEEE International Conference on Bioinformatics and Biomedicine(BIBM). Washington, DC, USA: IEEE, 2015:649-652. doi:10. 1109/BIBM.2015.7359761.

    [37] DONG Xishuang, CHOWDHURY S, QIAN Lijun, et al. Transfer Bi-directional LSTM RNN for named entity recognition in Chinese electronic medical records[C]// 2017 IEEE the 19th International Conference on e-Health Networking, Applications and Services(Healthcom). Dalian,China:IEEE, 2017: 1-4. doi:10. 1109/HealthCom.2017.8210840.

    [39] GRAVES A, SCHMIDHUBER J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures[J]. Neural Networks, 2005, 18(5-6):602-610.

    [40] HUANG Zhiheng,XU Wei,YU Kai. Bidirectional LSTM-CRF models for sequence tagging[DB/OL]. [2023-12-26]. https://arxiv. org/abs/ 1508.01991.

    [41] ASHISH V,NOAM S,NIKI P. Attention is all you need[DB/OL]. [2023-12-26]. https://arxiv.org/abs/ 1706.03762.

    [42] TONG Weiyue. Named entity recognition of power communication planning based on transformer[C]// 2022 IEEE the 10th Joint International Information Technology and Artificial Intelligence Conference(ITAIC). Chongqing,China:IEEE, 2022:588-592. doi: 10. 1109/ITAIC54216.2022.9836600.

    [45] DEVLIN J, CHANG Mingwei, LEE K. Bert: pre-training of deep bidirectional transformers for language understanding[C]// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Minneapolis,Minnesota:ACL, 2019:4171-4186. doi:10. 18653/v 1/N19-1423.

    [46] LI Hui, YU Lin, LYU Ming, et al. Fusion deep learning and machine learning for multi-source heterogeneous military entity recognition[C]// 2021 IEEE Conference on Telecommunications, Optics and Computer Science(TOCS). Shenyang, China: IEEE, 2021:535-539. doi:10. 1109/TOCS53301.2021.9688813.

    [48] HU Jiangyi, YANG Wenqing, YANG Huafei, et al. Named entity recognition method for power equipment based on BERT- BiLSTM-CRF[C]// 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing,Intl Conf on Cloud and Big Data Computing,Intl Conf on Cyber Science and Technology Congress(DASC/PiCom/ CBDCom/CyberSciTech). Falerna,Italy:IEEE, 2022: 1-6. doi:10. 1109/DASC/PiCom/CBDCom/Cy55231.2022.9927964.

    [49] EGAN T M, YANG Baiyin, BARTLETT K R. The effects of organizational learning culture and job satisfaction on motivation to transfer learning and turnover intention[J]. Human Resource Development Quarterly, 2004, 15(3): 279-301. doi: 10. 1002/hrdq. 1104.

    [50] NA Qionglan, SU Dan,ZHANG Jiaojiao, et al. A transfer learning based model for knowledge graph in power grid[C]// 2022 the 4th International Academic Exchange Conference on Science and Technology Innovation(IAECST). Guangzhou, China: IEEE, 2022: 1524-1527. doi:10. 1109/IAECST57965.2022. 10061887.

    [51] GUO Wenming, LU Junda, HAN Fang. Named entity recognition for Chinese electronic medical records based on multitask and transfer learning[J]. IEEE Access, 2022( 10):77375-77382. doi:10. 1109/ACCESS.2022.3192866.

    [52] YU Xin, HU Wenshen, LU Sha, et al. BioBERT based named entity recognition in electronic medical record[C]// 2019 the 10th International Conference on Information Technology in Medicine and Education(ITME). Qingdao,China:IEEE, 2019:49-52. doi: 10. 1109/ITME.2019.00022.

    [53] KANG Keming,TIAN Shengwei,YU Long. Named entity recognition of local adverse drug reactions in Xinjiang based on transfer learning[J]. Journal of Intelligent & Fuzzy Systems, 2021,40(5):8899-8914. doi:10.3233/JIFS-201017.

    [54] GINA A L. The third international Chinese language processing bakeoff: word segmentation and named entity recognition[C]// Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing. Sydney,Australia:ACL, 2006: 108-117.

    [55] PENG Nanyuan, DREDZE M. Named entity recognition for Chinese social media with jointly trained embeddings[C]// Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Lisbon, Portugal:ACL, 2015: 548-554. doi:10. 18653/v 1/D15-1064.

    [56] RALPH W, MARTHA P, MITCHELL M. Ontonotes release 4.0[M]. Philadelphia: Linguistic Data Consortium, 2011. doi: org/ 10.35111/gfjf-7r50.

    [57] ZHANG Yue, YANG Jie. Chinese NER using lattice LSTM[C]// Proceedings of the 56th Annual Meeting of Association for Computational Linguistics. Melbourne,Australia:ACL, 2018: 1554-1564. doi:10. 18653/v 1/P18-1144.

    [58] RALPH W, MARTHA P, MITCHELL M. Ontonotes release 5.0[M]. Philadelphia: Linguistic Data Consortium, 2013. doi: org/ 10.35111/xmhb-2b84.

    [59] XU Liang, DONG Qianqian, LIAO Yixuan, et al. CLUENER2020: fine-grained named entity recognition dataset and benchmark for Chinese[DB/OL]. [2023-12-26]. https://arxiv.org/abs/2001.04351.

    [60] XU Liang, HU Hai, ZHANG Xuanwei, et al. Clue: a Chinese language understanding evaluation benchmark[DB/OL]. [2023-12- 26]. https://arxiv.org/abs/2004.05986.

    [61] YAN Hang,DENG Bocao,LI Xiaonan,et al. Tener:adapting transformer encoder for named entity recognition[DB/OL]. [2023-12- 26]. https://arxiv.org/abs/ 1911.04474.

    [62] SUI Dianbo, CHEN Yubo, LIU Kang, et al. Leverage lexical knowledge for Chinese named entity recognition via collaborative graph network[C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Hong Kong, China: ACL, 2019: 3830-3840. doi: 10. 18653/v 1/ D19-1396.

    [63] XUAN Zhenyu, BAO Rui, JIANG Shengyi. FGN: Fusion Glyph Network for Chinese named entity recognition[C]// Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence. Singapore: Springer, 2021: 28-40. doi: 10. 1007/ 978-981-16-1964-9_3.

    [64] WU Shuang, SONG Xiaoming, FENG Zhenhua. MECT: Multi-metadata Embedding based Cross-Transformer for Chinese named entity recognition[C]// Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. [S. l.]: ACL, 2021: 1529-1539. doi: 10. 18653/v 1/2021. acl- long. 121.

    [65] LI Jingye,FEI Hao,LIU Jiang,et al. Unified named entity recognition as word-word relation classification[C]// Proceedings of the 36th AAAI Conference on Artificial Intelligence. Virtual, Online: Association for the Advancement of Artificial Intelligence, 2022: 10965-10973. doi:https://doi.org/ 10. 1609/aaai.v36i 10.21344.

    [66] QI Pengnian, QIN Biao. SSMl: Semantic Similarity and Mutual Information maximization based enhancement for Chinese NER [C]// Proceedings of the AAAl Conference on Artificial Intelligence. Washington, DC, USA:[s.n.], 2023: 1-9. doi:https://doi. org/ 10. 1609/aaai.v37i 11.26580.

    [67] GUI Tao,ZOU Yicheng,ZHANG Qi,et al. A lexicon-based graph neural network for Chinese NER [C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Hong Kong, China: Association for Computational Linguistics, 2019: 1040-1050. doi: 10. 18653/v 1/D19- 1096.

    [68] LI Xiaonan, YAN Huang, QIU Xipeng, et al. FLAT: Chinese NER using flat-lattice transformer[DB/OL]. [2023-12-26]. https:// arxiv.org/abs/2004. 11795.

    [69] WU Shuang, SONG Xiaoning, FENG Zhenhua, et al. NFLAT: non-flat-lattice transformer for Chinese named entity recognition [DB/OL]. [2023-12-26]. https://arxiv.org/abs/2205.05832.

    [70] NIE Yuyang,TIAN Yuanhe,SONG Yan,et al. Improving named entity recognition with attentive ensemble of syntactic information [DB/OL]. [2023-12-26]. https://arxiv.org/abs/2010. 15466.

    [71] ZHU Enwei, LI Jinpeng. Boundary smoothing for named entity recognition[DB/OL]. [2023-12-26]. https://arxiv. org/abs/ 2204. 12031.

    [72] ZHU Yuying,WANG Guoxin,KARLSSON B F. CAN-NER:convolutional attention network for Chinese named entity recognition [DB/OL]. [2023-12-26]. https://arxiv.org/abs/ 1904.02141.

    [73] HU Dou,WEI Lingwei. SLK-NER:exploiting second-order lexicon knowledge for Chinese NER [DB/OL]. [2023-12-26]. https:// arxiv.org/abs/2007.08416.

    MA Yijie, LAI Haiguang, LIU Ziwei, YANG Nan, ZHANG Gengxin. Overview of the research progress in entity recognition technology[J]. Journal of Terahertz Science and Electronic Information Technology , 2024, 22(5): 503
    Download Citation