Smart Agriculture ›› 2023, Vol. 5 ›› Issue (1): 122-131.doi: 10.12133/j.smartag.SA202303001
• Information Processing and Decision Making • Previous Articles Next Articles
JI Jie1,2(), JIN Zhou1, WANG Rujing1,2(), LIU Haiyan1,2, LI Zhiyuan1,2
Received:
2023-03-03
Online:
2023-03-30
CLC Number:
JI Jie, JIN Zhou, WANG Rujing, LIU Haiyan, LI Zhiyuan. Progressive Convolutional Net Based Method for Agricultural Named Entity Recognition[J]. Smart Agriculture, 2023, 5(1): 122-131.
Add to citation manager EndNote|Ris|BibTeX
URL: http://www.smartag.net.cn/EN/10.12133/j.smartag.SA202303001
Table 6
Comparison of the NER experimental results on public datasets — based on BERT
模型 | PeopleDaily | MSRA | ||||
---|---|---|---|---|---|---|
P/% | R/% | F1/% | P/% | R/% | F1/% | |
BERT | 93.81 | 94.12 | 93.97 | 94.48 | 93.78 | 94.12 |
Sesame | 86.05 | 85.53 | 85.79 | 88.76 | 87.18 | 87.96 |
JAM | 90.25 | 90.88 | 90.57 | 90.47 | 91.52 | 90.99 |
BERT- BiLSTM | 93.77 | 94.36 | 94.07 | 94.16 | 87.18 | 94.55 |
本文模型 | 94.53 | 94.44 | 94.48 | 94.04 | 94.89 | 94.96 |
1 | QIU X P, SUN T X, XU Y G, et al. Pre-trained models for natural language processing: A survey[J]. Science China technological sciences, 2020, 63(10): 1872-1897. |
2 | SEVASTJANOVA R, KALOULI A, BECK C, et al. Explaining contextualization in language models using visual analytics[C]// 2021 59th Association for Computational Linguistics (ACL). Stroudsburg, PA, USA: Association for Computational Linguistics, 2021: 464-476. |
3 | DEVLIN J, CHANG M-W, LEE K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding[C]// North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT). Stroudsburg, PA, USA: Association for Computational Linguistics, 2019: 4171-4186. |
4 | VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]// 2017 31st Annual Conference on Neural Information Processing Systems (NIPS). La Jolla, California, USA: Neural Information Processing Systems, 2017: 6000-6100. |
5 | 杨飘, 董文永. 基于BERT嵌入的中文命名实体识别方法[J]. 计算机工程, 2020, 46(4): 40-45, 52. |
YANG P, DONG W Y. Chinese named entity recognition method based on BERT embedding[J]. Computer engineering, 2020, 46(4): 40-45, 52. | |
6 | GAN Y, YANG R S, ZHANG C F, et al. Chinese named entity recognition based on BERT-transformer-BiLSTM-CRF model[C]// 2021 7th International Symposium on System and Software Reliability (ISSSR). Piscataway, NJ, USA: IEEE, 2021: 109-118. |
7 | GAO W C, ZHENG X H, ZHAO S S. Named entity recognition method of Chinese EMR based on BERT-BiLSTM-CRF[J]. Journal of physics. Conference series. 2021, 1848(1): ID 012083. |
8 | CHANG Y, KONG L, JIA K J, et al. Chinese named entity recognition method based on BERT[C]// 2021 IEEE International Conference on Data Science and Computer Application (ICDSCA). Piscataway, NJ, USA: IEEE, 2021: 294-299. |
9 | LI X, YAN H, QIU X, et al . FLAT: Chinese NER Using Flat-Lattice Transformer; proceedings of the ACL, F, 2020[C]// 2020 58th Annual Meeting of the Association for Computational Linguistics (ACL). Stroudsburg, PA, USA: Association for Computational Linguistics, 2020: 6836-6842. |
10 | 琚生根, 李天宁, 孙界平. 基于关联记忆网络的中文细粒度命名实体识别[J]. 软件学报, 2021, 32(8): 2545-2556. |
JU S G, LI T N, SUN J P. Chinese fine-grained name entity recognition based on associated memory networks[J]. Journal of software, 2021, 32(8): 2545-2556. | |
11 | WANG X Y, JIANG Y, BACH N, et al. Improving named entity recognition by external context retrieving and cooperative learning[J/OL]. arXiv: , 2021. |
12 | NIE Y Y, TIAN Y H, SONG Y, et al. Improving named entity recognition with attentive ensemble of syntactic information[C]// Findings of the Association for Computational Linguistics: EMNLP 2020. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020: 4231-4245. |
13 | 李林, 周晗, 郭旭超, 等. 基于多源信息融合的中文农作物病虫害命名实体识别[J]. 农业机械学报, 2021, 52(12): 253-263. |
LI L, ZHOU H, GUO X C, et al. Named entity recognition of diseases and insect pests based on multi source information fusion[J]. Transactions of the Chinese society for agricultural machinery, 2021, 52(12): 253-263. | |
14 | 赵鹏飞, 赵春江, 吴华瑞, 等. 基于注意力机制的农业文本命名实体识别[J]. 农业机械学报, 2021, 52(1): 185-192. |
ZHAO P F, ZHAO C J, WU H R, et al. Named entity recognition of Chinese agricultural text based on attention mechanism[J]. Transactions of the Chinese society for agricultural machinery, 2021, 52(1): 185-192. | |
15 | JAWAHAR G, SAGOT B, SEDDAH D. What does BERT learn about the structure of language? [C]// 2019 57th Annual Meeting of the Association for Computational Linguistics (ACL). Stroudsburg, PA, USA: Association for Computational Linguistics, 2019: 3651-3657. |
16 | ROGERS A, KOVALEVA O, RUMSHISKY A. A primer in BERTology: What we know about how BERT works[J]. Transactions of the association for computational linguistics, 2020, 8: 842-866. |
17 | JIE Z M, LU W. Dependency-guided LSTM-CRF for named entity recognition[C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2019: 4231-4245. |
18 | ZHANG Z B, WU S, JIANG D W, et al. BERT-JAM: Maximizing the utilization of BERT for neural machine translation[J]. Neurocomputing, 2021, 460: 84-94. |
19 | SU T C, CHENG H C. SesameBERT: Attention for anywhere[C]// 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA). Piscataway, NJ, USA: IEEE, 2020: 363-369. |
20 | HU J, SHEN L, ALBANIE S, et al. Squeeze-and-excitation networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2020, 42(8): 2011-2023. |
21 | JIANG Z, YU W, ZHOU D, et al. ConvBERT: Improving BERT with Span-based Dynamic Convolution[J/OL]. arXiv:2008.02496 [cs.CL], 2020. |
22 | SUN C J, GUAN Y, WANG X L, et al. Rich features based Conditional Random Fields for biological named entities recognition[J]. Computers in biology and medicine, 2007, 37(9): 1327-1333. |
23 | WEI J, REN X, LI X, et al. NEZHA: Neural contextualized representation for Chinese language understanding[J/OL]. arXiv:1909.00204v3 [cs.CL], 2009. |
24 | CUI Y M, CHE W X, LIU T, et al. Pre-training with whole word masking for Chinese BERT[J]. IEEE/ACM transactions on audio, speech and language processing, 2021, 29: 3504-3514. |
[1] | MAO Kebiao, ZHANG Chenyang, SHI Jiancheng, WANG Xuming, GUO Zhonghua, LI Chunshu, DONG Lixin, WU Menxin, SUN Ruijing, WU Shengli, JI Dabin, JIANG Lingmei, ZHAO Tianjie, QIU Yubao, DU Yongming, XU Tongren. The Paradigm Theory and Judgment Conditions of Geophysical Parameter Retrieval Based on Artificial Intelligence [J]. Smart Agriculture, 2023, 5(2): 161-171. |
[2] | XIA Xue, CHAI Xiujuan, ZHANG Ning, ZHOU Shuo, SUN Qixin, SUN Tan. A Lightweight Fruit Load Estimation Model for Edge Computing Equipment [J]. Smart Agriculture, 2023, 5(2): 1-12. |
[3] | GUO Yangyang, DU Shuzeng, QIAO Yongliang, LIANG Dong. Advances in the Applications of Deep Learning Technology for Livestock Smart Farming [J]. Smart Agriculture, 2023, 5(1): 52-65. |
[4] | ZUO Min, HU Tianyu, DONG Wei, ZHANG Kexin, ZHANG Qingchuan. Forecast and Analysis of Agricultural Products Logistics Demand Based on Informer Neural Network: Take the Central China Aera as An Example [J]. Smart Agriculture, 2023, 5(1): 34-43. |
[5] | HU Songtao, ZHAI Ruifang, WANG Yinghua, LIU Zhi, ZHU Jianzhong, REN He, YANG Wanneng, SONG Peng. Extraction of Potato Plant Phenotypic Parameters Based on Multi-Source Data [J]. Smart Agriculture, 2023, 5(1): 132-145. |
[6] | LIU Xiaohang, ZHANG Zhao, LIU Jiaying, ZHANG Man, LI Han, FLORES Paulo, HAN Xiongzhe. Infield Corn Kernel Detection and Counting Based on Multiple Deep Learning Networks [J]. Smart Agriculture, 2022, 4(4): 49-60. |
[7] | XU Yulin, KANG Mengzhen, WANG Xiujuan, HUA Jing, WANG Haoyu, SHEN Zhen. Corn and Soybean Futures Price Intelligent Forecasting Based on Deep Learning [J]. Smart Agriculture, 2022, 4(4): 156-163. |
[8] | LUO Qing, RAO Yuan, JIN Xiu, JIANG Zhaohui, WANG Tan, WANG Fengyi, ZHANG Wu. Multi-Class on-Tree Peach Detection Using Improved YOLOv5s and Multi-Modal Images [J]. Smart Agriculture, 2022, 4(4): 84-104. |
[9] | SHANG Fengnan, ZHOU Xuecheng, LIANG Yingkai, XIAO Mingwei, CHEN Qiao, LUO Chendi. Detection Method for Dragon Fruit in Natural Environment Based on Improved YOLOX [J]. Smart Agriculture, 2022, 4(3): 120-131. |
[10] | ZHANG Zhibo, ZHAO Xining, GAO Xiaodong, ZHANG Li, YANG Menghao. Accurate Extraction of Apple Orchard on the Loess Plateau Based on Improved Linknet Network [J]. Smart Agriculture, 2022, 4(3): 95-107. |
[11] | HE Ruimin, ZHENG Kefeng, WEI Qinyang, ZHANG Xiaobin, ZHANG Jun, ZHU Yihang, ZHAO Yiying, GU Qing. Identification and Counting of Silkworms in Factory Farm Using Improved Mask R-CNN Model [J]. Smart Agriculture, 2022, 4(2): 163-173. |
[12] | ZHUANG Jiayu, XU Shiwei, LI Yang, XIONG Lu, LIU Kebao, ZHONG Zhiping. Supply and Demand Forecasting Model of Multi-Agricultural Products Based on Deep Learning [J]. Smart Agriculture, 2022, 4(2): 174-182. |
[13] | SHAO Mingyue, ZHANG Jianhua, FENG Quan, CHAI Xiujuan, ZHANG Ning, ZHANG Wenrong. Research Progress of Deep Learning in Detection and Recognition of Plant Leaf Diseases [J]. Smart Agriculture, 2022, 4(1): 29-46. |
[14] | YANG Guofeng, HE Yong, FENG Xuping, LI Xiyao, ZHANG Jinnuo, YU Zeyu. Methods and New Research Progress of Remote Sensing Monitoring of Crop Disease and Pest Stress Using Unmanned Aerial Vehicle [J]. Smart Agriculture, 2022, 4(1): 1-16. |
[15] | LI Zhijun, YANG Shenghui, SHI Deshuai, LIU Xingxing, ZHENG Yongjun. Yield Estimation Method of Apple Tree Based on Improved Lightweight YOLOv5 [J]. Smart Agriculture, 2021, 3(2): 100-114. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||