Proceedings of the 2022 International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2022)

<Previous Article In Volume
Next Article In Volume>

Research on Chinese Named Entity Recognition Based on RoBERTa-BIGRU-MRC Model

Authors
Huai Peng1, Xianghong Tang1, *
1Key Laboratory of Public Big Data, School of Computer Science and Technology, Guizhou University, Guiyang, 550000, China
*Corresponding author. Email: xhtang@gzu.edu.cn
Corresponding Author
Xianghong Tang
Available Online 2 December 2022.
DOI
10.2991/978-94-6463-010-7_32How to use a DOI?
Keywords
Nested Entities; Entity Recognition; Word Vector; Machine Reading Comprehension
Abstract

Nested entity is the focus and difficulty of Chinese named entity recognition. The existing methods regard nested NER as two subtasks of Chinese word segmentation and sequence annotation. This method depends very much on the quality of input word vector, and low-quality word vector will lead to the error propagation of the model. To solve the above problems, a Chinese named entity recognition model based on RoBERTa-BiGRU-MRC is proposed. Firstly, RoBERTa is used to embed the entity type description and sentences to obtain the dynamic word vector. Secondly, BiGRU is used to extract contextual semantic features for further understanding of semantic information. Then two binary classifiers are constructed to better predict the probability value of the index at the beginning and end of the entity. Finally, the accuracy of the model is improved by constructing the loss function optimizer of predicted value and real value. Experiments were conducted on Chinese MSRA and onto4 data sets, and the accuracy, recall and F1 value were used as evaluation indexes. The experimental results show that the F1 value of the optimized model is 0.41 and 0.36 higher than that of the traditional sequence annotation model, respectively.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 2022 International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2022)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
2 December 2022
ISBN
10.2991/978-94-6463-010-7_32
ISSN
2589-4919
DOI
10.2991/978-94-6463-010-7_32How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Huai Peng
AU  - Xianghong Tang
PY  - 2022
DA  - 2022/12/02
TI  - Research on Chinese Named Entity Recognition Based on RoBERTa-BIGRU-MRC Model
BT  - Proceedings of the 2022 International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2022)
PB  - Atlantis Press
SP  - 308
EP  - 319
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-010-7_32
DO  - 10.2991/978-94-6463-010-7_32
ID  - Peng2022
ER  -