A Levitated Controlled Attention for Named Entity Recognition

Yang Y, Wu Z, Yang Y, Lian S, Guo F, Wang Z. A survey of information extraction based on deep learning. Appl Sci. 2022;12(19):9691.

Article  Google Scholar 

Xue L, Qing S, Pengzhou Z. Relation extraction based on deep learning. In: Proceedings of the ICIS ’18. IEEE, 2018;687–691.

Liu J, Min L, Huang X. An overview of event extraction and its applications. arXiv:2111.03212, 2021.

Li Q, Li J, Sheng J, Cui S, Wu J, Hei Y, Peng H, Guo S, Wang L, Beheshti A, et al. A survey on deep learning event extraction: approaches and applications. IEEE Trans Neural Netw Learn Syst. 2022.

Zhang X, Li D, Wu X. Parsing named entity as syntactic structure. In: Proceedings of the ISCA ’14. 2014;278–282.

Brill E, Mooney RJ. An overview of empirical natural language processing. AI Mag. 1997;18(4):13–13.

Google Scholar 

Chen Y, Huang R, Pan L, Huang R, Zheng Q, Chen P. A controlled attention for nested named entity recognition. Cognit Comput. 2023;15(1):132–45.

Article  Google Scholar 

Luo G, Yuan Q, Li J, Wang S, Yang F. Artificial intelligence powered mobile networks: from cognition to decision. IEEE Netw. 2022;36(3):136–44.

Article  Google Scholar 

Zhang J, Shen D, Zhou G, Su J, Tan C-L. Enhancing hmm-based biomedical named entity recognition by studying special phenomena. Biomed Inf. 2004;37(6):411–22.

Article  Google Scholar 

Kim J-H, Woodland PC. A rule-based named entity recognition system for speech input. In: Proceedings of ICSLP ’20. 2000;1:528–531.

D. Hanisch, K. Fundel, H.-T. Mevissen, R. Zimmer, J. Fluck. Prominer: rule-based protein and gene entity recognition. BMC Bioinf. 2005;6(1):1–9.

Google Scholar 

Zhou G, Zhang J, Su J, Shen D, Tan C. Recognizing names in biomedical texts: a machine learning approach. Bioinformatics. 2004;20(7):1178–90.

Article  Google Scholar 

Goller C, Kuchler A. Learning task-dependent distributed representations by backpropagation through structure. In: Proceedings of the ICNN ’96. vol. 1. IEEE. 1996:347–352.

Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;vol. 9, no. 8, pp. 1735–1780, 11.

Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P. Natural language processing (almost) from scratch. Mach Learn Res. 2011;12:2493–537.

Google Scholar 

Ju M, Miwa M, Ananiadou S. A neural layered model for nested named entity recognition. In: Proceedings of the ACL ’18. 2018;1446–1459.

Straková J, Straka M, Hajic J (2019) Neural architectures for nested NER through linearization. In: Proceedings of the ACL ’19. Florence, Italy: ACL. 2019:5326–5331.

Shibuya T, Hovy E. Nested named entity recognition via second-best sequence learning and decoding. Trans Assoc Comput Linguistics. 2020;8:605–20.

Article  Google Scholar 

Wang J, Shou L, Chen K, Chen G. Pyramid: a layered model for nested named entity recognition. In: Proceedings of the ACL ’20. 2020:5918–5928.

Ouchi H, Suzuki J, Kobayashi S, Yokoi S, Kuribayashi T, Konno R, Inui K. Instance-based learning of span representations: a case study through named entity recognition. In: Proceedings of the ACL ’20. 2020:6452–6459.

Xia C, Zhang C, Yang T, Li Y, Du N, Wu X, Fan W, Ma F, Yu P. Multi-grained named entity recognition. In: Proceedings of the ACL ’19. 2019:1430–1440.

Li J, Fei H, Liu J, Wu S, Zhang M, Teng C, Ji D, Li F. Unified named entity recognition as word-word relation classification. In: Proceedings of the AAAI ’22. 2022;36(10):10 965–10 973.

Zheng C, Cai Y, Xu J, Leung H-f, Xu G (2019) A boundary-aware neural model for nested named entity recognition. In: Proceedings of the EMNLP ’19. ACL. 2019:357–366.

Tan C, Qiu W, Chen M, Wang R, Huang F. Boundary enhanced neural span classification for nested named entity recognition. In: Proceedings of the AAAI ’20. 2020;34(05):9016–9023.

Tang M, He Y, Xu Y, Xu H, Zhang W, Lin Y. A boundary offset prediction network for named entity recognition. In: Proceedings of the EMNLP ’23. ACL. 2023:14 834–14 846.

Baldini Soares L, FitzGerald N, Ling J, Kwiatkowski T. Matching the blanks: distributional similarity for relation learning. In: Proceedings of the ACL ’19. 2019:2895–2905.

Ye D, Lin Y, Li P, Sun M. Packed levitated marker for entity and relation extraction. In: Proceedings of the ACL ’22. 2022:4904–4917.

Zhong Z, Chen D. A frustratingly easy approach for entity and relation extraction. In: Proceedings of the NAACL ’21. 2021:50–61.

Yu J, Bohnet B, Poesio M. Named entity recognition as dependency parsing. In Proceedings of the ACL ’20. 2020:6470–6476.

Wang Y, Li Y, Tong H, Zhu Z. Hit: nested named entity recognition via head-tail pair and token interaction. In: Proceedings of the EMNLP ’20. 2020:6027–6036.

Huang P, Zhao X, Hu M, Tan Z, Xiao W. T2-NER: a two-stage span-based framework for unified named entity recognition with templates. Trans Assoc Comput Linguistics. 2023;11:1265–82.

Article  Google Scholar 

Yu J, Chen Y, Zheng Q, Wu Y, Chen P. Full-span named entity recognition with boundary regression. Connec Sci. 2023;35(1):2181483.

Article  Google Scholar 

Han R, Peng T, Yang C, Wang B, Liu L, Wan X. Is information extraction solved by ChatGPT? An analysis of performance, evaluation criteria, robustness and errors. arXiv:2305.14450, 2023.

Wang S, Sun X, Li X, Ouyang R, Wu F, Zhang T, Li J, Wang G. GPT-NER: named entity recognition via large language models. arXiv:2304.10428, 2023.

Devlin J, Chang M-W, Lee K, Toutanova K. Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the NAACL ’19. 2019;1:4171–4186.

Yongming N, Yanping C, Yongbin Q, Ruizhang H, Ruixue T, Ying H. A joint model for entity boundary detection and entity span recognition. King Saud University-Comput Inf Sci. 2022;34(10):8362–9.

Google Scholar 

Ainslie J, Ontanon S, Alberti C, Cvicek V, Fisher Z, Pham P, Ravula A, Sanghai S, Wang Q, Yang L. ETC: encoding long and structured inputs in transformers. In: Proceedings of the EMNLP ’20. ACL. 2020:268–284.

Doddington G, Mitchell A, Przybocki M, Ramshaw L, Strassel S, Weischedel R. The automatic content extraction (ace) program-tasks, data, and evaluation. In: Proceedings of the LREC ’05, 2005.

Lu W, Roth D. Joint mention extraction and classification with mention hypergraphs. In: Proceedings of the EMNLP ’15. ACL. 2015:857–867.

Tjong EF, Sang K, De Meulder F. Introduction to the CoNLL-2003 shared task: language-independent named entity recognition. In: Proceedings of the HLT-NAACL ’03. 2003:142–147.

Peng N, Dredze M. Named entity recognition for Chinese social media with jointly trained embeddings. In: Proceedings of the EMNLP ’15. ACL. 2015:548–554.

Kingma D, Ba J. Adam: a method for stochastic optimization. In: Proceedings of the ICLR ’15, San Diega, CA, USA, 2015.

Shen Y, Ma X, Tan Z, Zhang S, Wang W, Lu W. Locate and label: a two-stage identifier for nested named entity recognition. In: Proceedings of the ACL-IJCNLP ’21. 2021:2782–2794.

Shen Y, Wang X, Tan Z, Xu G, Xie P, Huang F, Lu W, Zhuang Y. Parallel instance query network for named entity recognition. In: Proceedings of the ACL ’22. 2022:947–961.

Shen Y, Song K, Tan X, Li D, Lu W, Zhuang Y. Diffusionner: boundary diffusion for named entity recognition. In: Proceedings of the ACL ’23. 2023:3875–3890.

Tan Z, Shen Y, Zhang S, Lu W, Zhuang Y. A sequence-to-set network for nested named entity recognition. In: Proceedings of the IJCAI ’21, 2021.

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