Academic Journal of Computing & Information Science, 2022, 5(1); doi: 10.25236/AJCIS.2022.050109.
Tao Li1, Xuhan Jin2
1School of Innovation and Entrepreneurship, Huaiyin Institute of Technology, Jiangsu, China
2School of Mechanical and Electrical Engineering, Anhui Jianzhu University, Anhui, China
Named entity recognition is an important research direction of natural language processing. This paper first reviews the development process and main stages of named entity recognition, then expounds the research contents and methods of named entity recognition, and puts forward the key and difficult points of named entity recognition, The development process of named entity recognition is from the initial rule-based and dictionary based method to the later statistical learning method, and then to the mixed learning method and some popular learning methods. Finally, the development of named entity recognition is prospected.
Name entity identifying, Natural language processing, Artificial intelligence
Tao Li, Xuhan Jin. Overview of Naming Entities Based on Natural Language Processing. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 1: 44-48. https://doi.org/10.25236/AJCIS.2022.050109.
[1] Chinchor N,Robinso N P.MUC-7 Named Entity Task Definition [C] //Proceedings of the 7th Conference on Message Understanding,1997,29: 1-21.
[2] Babych B, Hartley A. Improving Machine Translation Quality with Automatic Named Entity Recognition [C] //Proceedings of the 7th International EAMT Workshop on MT and Other Language Technology Tools, Improving MT Through Other Language Technology Tools, Resource and Tools for Building MT at EACL 2003, 2003.
[3] Bordes A, Usuniern, Chopra S, et al. Large-scale Simple Question Answering with Memory Networks [J]. arXiv preprint arXiv: 1506.02075, 2015.
[4] Riedel S,Yao L, Mccallum A, et al. Relation Extraction with Matrix Factorization and Universal Schemas [C] //Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2013: 74-84.
[5] Shen W, Wang J, Luo P, et al. Linden: Linking Named Entities with Knowledge Base Via Semantic Knowledge [C] //Proceedings of the 21st International Conference on World Wide Web, 2012: 449-458.
[6] Rau L F. Extractingcompany name from text [C] //Proceedings of the sevesth IEEE Conference on Artificial Intelligence Application. IEEE, 1991, 1: 29-32.
[7] Sun Maosong, Huang Changning, Gao Haiyan, etc. Automatic Identification of Chinese Names [J]. Chinese letter Journal, 1995, 9 (2): 16-27.
[8] Zhang Xiaoheng, Wang Lingling. Identification and Analysis of Chinese Institution Name [J]. Chinese information Report, 1997, 11 (4): 21-32.
[9] Bikel D M, Schwarta R, Weischedel R M. An Algorithm That Learns What's in a name [J]. Machine Learning Journalland Learning, 1999, 34 (1-3): 211-231.
[10] Lia W, Veeramachaneni S. A Simple Semi - Supervised Algorithm for named Entity Recognition [C]. In: Proceedings of the NaaCl HLT 2009 Workshop on Semi - Supervised Learning for Natural LANGUAGE Processing. 2009: 58-65.
[11] Ratinov L, Roth D. Design Challenges and Misconceptions in Named Entity Recognition [C]. In: Proceedings of the 13th Confer Ence on Computational Natural Language Learning. 2009: 147-155.
[12] Xie R, Liu Z, Jia J, et al. Representation Learning of Knowledge Graphs with Entity Descriptions [C] // Thirtieth AAAI Conference on Artificial 258 Radio Communications Technology Vol.46 No.3 2020 Intelligence, 2016.
[13] Ratnaparkhi A. A Maximum Entropy Model for Part-Of - Speech Tagning [C] // Conference on Empirical Methods in Natural Language Processing, 1996: 133-142.
[14] Lafferty J, McCallum A, Pereira F C N. Condi Tional Random Fields: probabilistic models for segmenting And labeling sequence data [c] //proceedings of the 18th International Conference On Machine Learning 2001 (ICML 2001): 282-289.
[15] Culotta A, McCallum A. Confidence Estimation for Information Extraction [C] // Proceedings of HLT-NaaCl 2004: SHORT PAPERS, 2004: 109-112.
[16] Carpenter B. Ling pipe for 99. 99% Recall of Gene Mentions [C] // Proceedings of the second biocreative CHALLENGE EVALE WORKSHOP. Biocreative, 2007, 23: 307-309.
[17] Minkov E, Wang R C, Tomasic A, et al. Ner Systems That Suit User's Preferences: Adjusting THE Recall-precision track - off for entity extraction [c] //Proceedings of the human language technology Conference of the NaaCl, Companion Volume: Short Pers. 2006: 93-96.
[18] Collobert R, Weston J, Bottou L, et al. Natural Language processing (almost) from scratch [j]. Journal of Machine Learning Research, 2011, 12 (AUG): 2493-2537.
[19] Chiu J P C, Nichols E. Named Entity Recognition with Bidirectional LSTM-CNNS [J]. TRANSACTIONS OF THE Associ at Computational Linguistics, 2016, 4: 357-370.
[20] Ma X, Hovy E. End - to - End Sequence Labeling Via Bi-Directional LSTM-CNNS-CRF [J]. Arxiv Preprint Arxiv: 1603.01354, 2016.
[21] Liu L, Shang J, Ren X, et al. Empower Sequern Labe Ling with Task - Aware Neural Language Model [C] //Thirty-Second Aaai Conference on Artificial Intelligence. 2018.
[22] Lin Y, Tsai T, Chou W, et al. A Maximum Entropy Approach to Biomedical named Entity Recognition [C]. In: Proceedings of The 4h ACM Sigkdd Workshop on Data Mining in BioInformatics. 2004.
[23] Greenberg N, Bansal T, Verga P, et al. Marginal Likelihood Training of Bilstm-CRF for Biomedical Named Entity Recognition from Disjoint Label Sets [C] // Pro Ceedings of the 2018 Conference ON Empirical Methods in Natural Language Processing, 2018: 2824-2829.
[24] Augenstein I, Ruder S, Sogaard A. Multi – Task Learning of Pairwise Sequence Classification Tasks over Disparate Label Spaces [J]. Arxiv Preprint Arxiv: 1802.09913, 2018.
[25] Bryozkin G, Drori Y, Gilon O, et al. A joint Named - Entity Recognizer for Heterogeneous Tag – Sets Using a tag hierarchy [j]. Arxiv Preprint Arxiv: 1905.09135, 2019.