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A Survey of Low-Resource Named Entity Recognition

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The 7th International Conference on Information Science, Communication and Computing (ISCC2023 2023)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 350))

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Abstract

Named Entity Recognition, as one of the typical tasks of information extraction, has a wide range of applications. However, the difficulty of data collection and the lack of data annotation are common problems in real scenarios. It is difficult for classical named entity recognition methods to fully obtain hidden information when the dataset and data labels are insufficient. In this case, the recognition accuracy will drop significantly. In resource-poor scenarios, the use of multi-task learning, data augmentation, and transfer learning methods can effectively utilize limited data resources or introduce data from other fields to optimize model performance. In this survey, firstly, we introduce the reader to the state of research on named entity recognition, and outline the reasons for the survey and the contribution of this paper. Then, we comprehensively describe the datasets and evaluation methods included in named entity recognition, and propose a Classification of named entity recognition in the case of lack of resources. Finally, we analyze the existing problems on the basis of the above, and further look forward to the future development direction.

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Acknowledgment

This work was supported by Hainan Provincial Natural Science Foundation of China (Grant No. 620MS021, 621QN211), National Natural Science Foundation of China (NSFC) (Grant No. 62162024, 62162022), the Key Research and Development Program of Hainan Province (Grant No. ZDYF2020040, ZDYF2021GXJS003), the Major science and technology project of Hainan Province (Grant No. ZDKJ2020012), Science and Technology Development Center of the Ministry of Education Industry-university-Research Innovation Fund (2021JQR017), the Key Laboratory of PK System Technologies Research of Hainan.

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Authors and Affiliations

  1. School of Computer Science and Technology, Hainan University, Haikou, 570228, China

    Xiangyan Tang, Dongwan Xia, Yajing Li & Taixing Xu

  2. College of Intelligence and Computing, Tianjin University, Tianjin, 300072, China

    Xiangyan Tang

  3. Hainan Blockchain Technology Engineering Research Center, Haikou, 570228, China

    Xiangyan Tang, Dongwan Xia, Yajing Li & Taixing Xu

  4. Department of Computer Science and Mathematics, Sul Ross State University, Alpine, TX, 79830, USA

    Neal N. Xiong

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  1. Xiangyan Tang
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  2. Dongwan Xia
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  3. Yajing Li
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  4. Taixing Xu
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  5. Neal N. Xiong
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Correspondence to Dongwan Xia .

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Editors and Affiliations

  1. School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecomm, Beijing, Beijing, China

    Xuesong Qiu

  2. Department of Computer Science, The University of Alabama, Tuscaloosa, AL, USA

    Yang Xiao

  3. Department of Electrical Engineering, Wright State University, Dayton, OH, USA

    Zhiqiang Wu

  4. Department of Informatics, University of Leicester, Leicester, UK

    Yudong Zhang

  5. School of Computer Engineering, Nanjing Institute of Technology, Nanjing, Jiangsu, China

    Yuan Tian

  6. School of Physics and Optoelectronic Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

    Bo Liu

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Tang, X., Xia, D., Li, Y., Xu, T., Xiong, N.N. (2024). A Survey of Low-Resource Named Entity Recognition. In: Qiu, X., Xiao, Y., Wu, Z., Zhang, Y., Tian, Y., Liu, B. (eds) The 7th International Conference on Information Science, Communication and Computing. ISCC2023 2023. Smart Innovation, Systems and Technologies, vol 350. Springer, Singapore. https://doi.org/10.1007/978-981-99-7161-9_19

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