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Academic Journal of Engineering and Technology Science, 2022, 5(4); doi: 10.25236/AJETS.2022.050407.

Research on Agricultural Named Entity Recognition Based on Pre Train BERT

Author(s)

Zhu Lun, Zhou Hui

Corresponding Author:
Zhou Hui
Affiliation(s)

School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, Changzhou University, Changzhou, 213164, Jiangsu, China

Abstract

Aiming at the problems of traditional named entity recognition methods relying on artificial dictionary and insufficient feature extraction in the process of agricultural pest information extraction, considering the complexity and fuzziness of agricultural text data, an agricultural named entity recognition method based on pre training BERT is proposed. firstly, the unlabeled pre training BERT was used to eliminate ambiguity, then BILSTM was used to capture long-distance dependence, and finally the best sequence annotation was selected through CRF. In addition, combined with the text particularity of agricultural entities, the word itself and partial radicals are selected to establish a joint multi feature PBERT-BILSTM-CRF for entity recognition. experiments show that the PBERT-BILSTM-CRF combined with the combined characteristics of the word itself and the radical has improved the precision, recall and F1 value compared with other models, and its optimal F1 value has reached 90.24%. the model has the characteristics of fast training speed and strong recognition ability. Named entity recognition is the premise of many tasks in the middle and downstream of natural language processing. The model provides a research basis for the construction of knowledge graph in agricultural field and agricultural Q&A.

Keywords

agricultural; named entity recognition; CRF; fine tuning model; feature selection

Cite This Paper

Zhu Lun, Zhou Hui. Research on Agricultural Named Entity Recognition Based on Pre Train BERT. Academic Journal of Engineering and Technology Science (2022) Vol. 5, Issue 4: 34-42. https://doi.org/10.25236/AJETS.2022.050407.

References

[1] ZHAO C J. Research on the development status and strategic objectives of smart agriculture [J]. China Agricultural Abstracts - agricultural engineering,2019.

[2] WANG C Y, WANG F. Research on agricultural named entity recognition based on conditional random field [J]. Journal of Hebei Agricultural University,2014,37(1):132-135. DOI:10.13320/j.cnki.jauh.2014.0027.

[3] LI J X, WANG P. A review of research methods of Chinese named entity recognition [J]. Computer Era,2021(4):18-21. DOI:10.16644/j.cnki.cn33-1094/tp.2021.04.005.

[4] Dong C,Zhang J,Zong Cet al. Character-Based LSTM-CRF with Radical-Level Features for Chinese Named Entity Recognition[J].Springer International Publishing,2016.

[5] WANG X M, TAO H C. Research on Chinese Named Entity Recognition Based on deep learning [J]. Journal of Chengdu University of information engineering,2020,35(3):264-270. DOI:10. 16836/j.cnki.jcuit.2020.03.003.

[6] Lample G,Ballesteros M,Subramanian S,et al.Neural Architectures for Named Entity Recognition[C]//Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.2016.

[7] ZHANG J, WU Q, YANG X Y,et al. Agricultural named entity recognition based on conditional random field [J]. Computer and modernization. 2018, No.269(01):123-126.

[8] LIU X J, GAO L C, SHI X Z. Named entity recognition based on Bi LSTM and attention mechanism[J]. Journal of Luoyang Institute of Technology (NATURAL SCIENCE EDITION) ,2019, 29(01):68-73+80.

[9] Qiang Z,Yong S B,Lz B,et al.Named entity recognition method in health preserving field based on BERT[J].Procedia Computer Science,2021,183:212-220.

[10] Cao, P. , Chen, Y. , Kang, L. , Zhao, J. , & Liu, S. . (2018). Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.

[11] ZHAO P F, ZHAO C J,WU H R, Named entity recognition of agricultural text based on attention mechanism[J]. Journal of agricultural machinery, 2021,52(1):185-192. DOI:10.6041/j.issn.1000-1298.2021.01.021.

[12] Devlin J,Chang M W,Lee K,et al.BERT:Pre-training of Deep Bidirectional Transformers for Language Understanding[J].2018.

[13] Guo X,Zhou H,Su J,et al.Chinese agricultural diseases and pests named entity recognition with multi-scale local context features and self-attention mechanism[J].Computers and Electronics in Agriculture, 2020, 179(5):105830.

[14] Cw A,Hong W,Hui Z A,et al.Chinese medical named entity recognition based on multi-granularity semantic dictionary and multimodal tree[J].Journal of Biomedical Informatics,2020,111.

[15] X Li,Zhang H,Zhou X H.Chinese Clinical Named Entity Recognition with Variant Neural Structures Based on BERT Methods[J]. Journal of Biomedical Informatics, 2020, 107(5):103422.

[16] LI F L,KE J. Research progress of word vector semantic representation [J]. Information science,2019,037(005):155-165.

[17] Vaswani A,Shazeer N,Parmar N,et al.Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.New York,2017:6000-6010.

[18] Hochreiter,Sepp,Schmidhuber,et al.Long short-term memory.[J].Neural Computation,1997.

[19] Luo, L. , Yang, Z. , Yang, P. , Zhang, Y. , Wang, L. , & Lin, H. , et al. (2017). An attention-based bilstm-crf approach to document-level chemical named entity recognition. Bioinformatics(8), 8.

[20] Xu G H,WANG c Y,HE X F.Improving clinical named entity recognition with global neural attention[c]//Asia·PacificWeb and Web·Age Informa“on Management Joint International Conference on Web and Big Data,2018:264—279.

[21] LI Ni,GuAN Huanmei,YANG Piao,et a1.BERT—IDcNN—CRF for named entity recogni“on in chinese[J]. Journal ofshandong university(Natural science),2020,55(1):102-109.