Abstract
In the process of Chinese rice variety information named entity recognition, traditional methods cannot extract potential semantic information from data and cannot capture long-distance dependence. So, this paper proposes a Chinese rice variety information named entity recognition method based on a bidirectional long short-term memory network and conditional random field (BiLSTM-CRF), which combines radical features, word segmentation boundary features, and multi-head attention mechanism. First, the radical features and word segmentation boundary features are encoded and integrated into a pre-trained character vector as the model embedding to solve the disadvantage of the lack of semantic information. Then, the multi-head attention mechanism is introduced to assist the bidirectional long short-term memory network (BiLSTM) in acquiring long-distance context-dependence. Finally, a conditional random field (CRF) is used to realize character-level sequence annotation and then realize the named entity recognition task of Chinese rice variety information. The experimental results show that this model’s precision, recall, and F1-score are 95.78%, 97.07%, and 96.42%, respectively. The three evaluation indices are better than those of the other models. The model proposed in this paper can effectively identify Chinese rice variety information entities and provides method support for the subsequent construction of a Chinese rice variety information knowledge graph.
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References
Ahmadianfar I, Heidari AA, Gandomi AH, Chu X, Chen H (2021) RUN Beyond the Metaphor: An Efficient Optimization Algorithm Based on Runge Kutta Method. Expert Syst Appl 181:115079
Barua J, Niyogi R (2020) Improving named entity recognition and disambiguation in news headlines. Int J Intell Inf Database Syst 12(4):279
Bengio Y, Glorot X (2010) Understanding the difficulty of training deep feed forward neural networks. Proc AISTATS 2010:249–256
Bingtao G, Yang Z, Bin L (2019) BioTrHMM: named entity recognition algorithm based on transfer learning in biomedical texts. Appl Res Comput 36(01):45–48
Brooke J, Hammond A, Baldwin T (2016) Bootstrapped Text-level Named Entity Recognition for Literature. In: Meeting of the Association for Computational Linguistics
Chen Ying, Gan Huimin, Zeng Zhuang, Chen Huiling (2022) DADCNet: Dual attention densely connected network for more accurate real iris region segmentation. Int J Intell Syst 37(1):829–858
Cho M et al (2020) Combinatorial feature embedding based on CNN and LSTM for biomedical named entity recognition. J Biomed Inform 103:103381
Dang TH et al (2018) D3NER: biomedical named entity recognition using CRF-BiLSTM improved with fine-tuned embeddings of various linguistic information. Bioinformatics 34(20):3539–3546
Fan X et al (2021) Recognition of corn diseases in complex background based on improved convolutional neural network. Trans Chinese Soc Agric Mach 52(03):210–217
Gajendran S, Manjula D, Sugumaran V (2020) Character level and word level embedding with bidirectional LSTM – Dynamic recurrent neural network for biomedical named entity recognition from literature. J Biomed Inform 112:103609
Guo K et al (2020) Toward anomaly behavior detection as an edge network service using a dual-task interactive guided neural network. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2020.3015987
Guo X et al (2020) Named entity recognition of pests and diseases based on radical embedding and attention mechanism. Trans Chinese Soc Agric Mach 51(S2):335–343
Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst Int J Escience 97:849–872
Hu B et al (2021) RRL-GAT: Graph Attention Network-driven Multi-Label Image Robust Representation Learning. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2021.3089180
Jiang N, Tian F, Li J, Yuan X, Zheng J (2020) MAN: mutual attention neural networks model for aspect-level sentiment classification in SIoT. IEEE Internet Things J 7(4):2901–2913
Jiang N, Xu D, Zhou J, Yan H, Wan T, Zheng J (2020) Toward optimal participant decisions with voting-based incentive model for crowd sensing. Inf Sci 512:1–17
Jiang N, Huang D, Chen J, Wen J, Zhang H, Chen H (2021) Semi-direct monocular visual-inertial Odometry using point and line features for IoV. ACM Trans Internet Technol (TOIT) 22(1):1–23
Jiang N et al (2021) SAN: attention-based social aggregation neural networks for recommendation system. Int J Intell Syst. https://doi.org/10.1002/int.22694
Kong J et al (2021) Incorporating multi-level CNN and attention mechanism for Chinese clinical named entity recognition. J Biomed Inform 116:103737
L, S et al (2020) A study on joint entity recognition and relation extraction for rice diseases pests weeds and drugs. J Nan**g Agric Univ 43(06):179–189
Li X et al (2017) Recognition of crops, diseases and pesticides named entities in chinese based on conditional random fields. Trans Chin Soc Agric Mach 48(s1):178–185
Li D, Tan W (2019) Research on named entity recognition method of plant attribute text. J Frontiers Comput Sci Technol 13(12):2085–2093
Li S, Chen H, Wang M, Heidari AA, Mirjalili S (2020) Slime mould algorithm: a new method for stochastic optimization. Futur Gener Comput Syst 111:300–323
Li Y et al (2021) Virtual Adversarial Training based Deep Feature Aggregation Network from Dynamic Effective Connectivity for MCI Identification. IEEE Trans Med Imaging 41:237–251. https://doi.org/10.1109/TMI.2021.3110829
Li Y et al (2021) Spatio-Temporal-Spectral Hierarchical Graph Convolutional Network With Semisupervised Active Learning for Patient-Specific Seizure Prediction. IEEE Trans Cybern PP:1–16. https://doi.org/10.1109/TCYB.2021.3071860
Li X et al (2021) Overview of CCKS 2020 Task 3: named entity recognition and event extraction in Chinese electronic medical records. Data Intelligence 3(03):376–388
Liang J et al (2017) A novel approach towards medical entity recognition in Chinese clinical text. J Healthc Eng 2017:1–16
Liang G, On BW, Jeong D, Heidari AA, Kim HC, Choi GS, Shi Y, Chen Q, Chen H (2021) A text GAN framework for creative essay recommendation. Knowl-Based Syst 232:107501
Liao F et al (2019) Combined self-attention mechanism for Chinese named entity recognition in military. Future Internet 11(8):180
Lu Y et al (2020) A military named entity recognition method based on pre-training language model and BiLSTM-CRF. J Phys Conf Ser 1693:012161
M, L. and K. F (2019) Social media named entity recognition integrated with self-attention mechanism. J Tsinghua Univ (Sci Technol) 59(06):461–467
Huang N, Huang He, Wang R (2017) Agriculture-related product name extraction and category labeling based on ontology and conditional random field. J Comput Appl 1:233–238
Qin Y, Zeng Y (2018) Research of clinical named entity recognition based on Bi-LSTM-CRF. J Shanghai Jiaotong Univ (Sci) 023(003):392–397
Qiu XP et al (2020) Pre-trained models for natural language processing: A Survey. Sci China Technol Sci 63(10):1872–1897
Qiu S et al (2021) Sensor Combination Selection Strategy for Kayak Cycle Phase Segmentation Based on Body Sensor Networks. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2021.3102856
Qiu S et al (2021) Sensor network oriented human motion capture via wearable intelligent system. Int J Intell Syst 37:1646–1673. https://doi.org/10.1002/int.22689
Saber A, Sakr M, Abo-Seida OM, Keshk A, Chen H (2021) A novel deep-learning model for automatic detection and classification of breast Cancer using the transfer-learning technique. IEEE Access 9:71194–71209
Santoso J et al (2021) Named entity recognition for extracting concept in ontology building on Indonesian language using end-to-end bidirectional long short term memory. Expert Syst Appl 176:114856
Tian Y et al (2021) Hierarchical self-adaptation network for multimodal named entity recognition in social media. Neurocomputing 439:12–21
Tu J, Chen H, Wang M, Gandomi AH (2021) The Colony predation algorithm. J Bionic Eng 18(3):674–710
Wang CY, Wang F (2014) Study on recognition of chinese agricultural named entity with conditional random fields. J Agric Univ Hebei 01:132–135
Wang T, Zhang X, Jiang R, Zhao L, Chen H, Luo W (2021) Video Deblurring via spatiotemporal pyramid network and adversarial gradient prior. Comput Vis Image Underst 203:103135
Wu Z, Wang R, Li Q, Lian X, Xu G, Chen E, Liu X (2020) A location privacy-preserving system based on query range cover-up for location-based services. IEEE Trans Veh Technol 69:5244–5254
Wu Z, Li R, Zhou Z, Guo J, Jiang J, Su X (2020) A user sensitive subject protection approach for book search service. J Assoc Inf Sci Technol 71(2):183–195
Wu Z, Shen S, Lian X, Su X, Chen E (2020) A dummy-based user privacy protection approach for text information retrieval. Knowl-Based Syst 195:105679
Wu Z, Li G, Shen S, Lian X, Chen E, Xu G (2021) Constructing dummy query sequences to protect location privacy and query privacy in location-based services. World Wide Web 24(1):25–49
Wu Z, Shen S, Zhou H, Li H, Lu C, Zou D (2021) An effective approach for the protection of user commodity viewing privacy in e-commerce website. Knowl-Based Syst 220:106952
**e C, Gao J, Chen J (2021) Named entity recognition for crop diseases and insect pests. J Inn Mong Agric Univ (Nat Sci Ed) 43(1):86–90
Xu G., Wang C., He X (2018) Improving Clinical Named Entity Recognition with Global Neural Attention. In: Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data
Xue X, Wang S, Zhang L, Feng Z, Guo Y (2019) Social learning evolution (SLE): computational experiment-based modeling framework of social manufacturing. IEEE Trans Ind Inform 15(6):3343–3355
Xue X et al (2020) Value Entropy: A Systematic Evaluation Model of Service Ecosystem Evolution. IEEE Trans Serv Comput. https://doi.org/10.1109/TSC.2020.3016660
Yang Y, Chen H, Heidari AA, Gandomi AH (2021) Hunger games search: visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Syst Appl 177:114864
Yin M et al (2019) Chinese clinical named entity recognition with radical-level feature and self-attention mechanism. J Biomed Inform 98:103289
Yu H, Liu J, Chen C, Heidari AA, Zhang Q, Chen H, Mafarja M, Turabieh H (2021) Corn leaf diseases diagnosis based on K-means clustering and deep learning. IEEE Access 9:143824–143835
Yuan XU et al (2018) Medical entity recognition and application of stroke admission records based on the combination of CRF and RUTA rules. J Sun Yat-sen Univ (Med Sci) 39(03):455–462
Zhang L, Wu H (2021) Medical text entity recognition based on deep learning. J Phys Conf Ser 1744(4):042209
Zhang X, Wang D, Zhou Z, Ma Y (2019) Robust low-rank tensor recovery with rectification and alignment. IEEE Trans Pattern Anal Mach Intell 43(1):238–255
Zhang L et al (2020) A Covert Communication Method Using Special Bitcoin Addresses Generated by Vanitygen. Comput Mater Contin 65(1):597–616
Zhang X et al (2020) Pyramid channel-based feature attention network for image dehazing. Comput Vis Image Underst 197:103003
Zhang L et al (2021) Resource Allocation and Trust Computing for Blockchain-Enabled Edge Computing System. Comput Sec 105:102249
Zhang L et al (2021) Research on a Covert Communication Model Realized by Using Smart Contracts in Blockchain Environment. IEEE Syst J:1–12. https://doi.org/10.1109/JSYST.2021.3057333
Zhang X, Jiang R, Wang T, Wang J (2021) Recursive neural network for video Deblurring. IEEE Trans Circuits Syst Video Technol 31(8):3025–3036
Zhang X, Wang J, Wang T, Jiang R, Xu J, Zhao L (2021) Robust feature learning for adversarial defense via hierarchical feature alignment. Inf Sci 560:256–270
Zhao P et al (2021) Named entity recognition of agricultural text based on attention mechanism. Trans Chin Soc Agric Mach 52(01):185–192
Zhu X et al (2021) Cross View Capture for Stereo Image Super-Resolution. IEEE Trans Multimedia:1. https://doi.org/10.1109/TMM.2021.3092571
Zhu X et al (2021) Lightweight Image Super-Resolution with Expectation-Maximization Attention Mechanism. IEEE Trans Circuits Syst Video Technol:1. https://doi.org/10.1109/TCSVT.2021.3078436
Zhuang H et al (2021) A bert based Chinese named entity recognition method on ASEAN News. J Phys Conf Ser 1848(1):012101
Yu Honggan, Tao Jianfeng, Qin Cheng**, Liu Mingyang, **ao Dengyu, Sun Hao, Liu Chengliang (2022) A novel constrained dense convolutional autoencoder and DNN-based semi-supervised method for shield machine tunnel geological formation recognition. Mechanical Systems and Signal Processing 165:108353. https://doi.org/10.1016/j.ymssp.2021.108353
Rastogi Somya, Choudhary Shivani (2019) Face recognition by using neural network. Acta Informatica Malaysia 3(2):07–09. https://doi.org/10.26480/aim.02.2019.07.09
Lee Wen Chiat, Nicholas Hoe K, Viswanathan Kuperan, Baharuddin Amir Hussin (2020) An economic analysis of anthropogenic climate change on rice production in Malaysia. Malaysian Journal of Sustainable Agriculture 4(1):01–04
Abd. Kharim Muhammad Nurfaiz, Wayayok Aimrun, Abdullah Ahmad Fikri, Shariff Abdul Rashid Mohamed (2020) Effect of variable rate application on rice leaves burn and chlorosis in system of rice intensification. Malaysian Journal of Sustainable Agriculture 4(2):66–70. https://doi.org/10.26480/mjsa.02.2020.66.70
Salleh Mohd Syahmi, Malek Ris Amirah, Shahari Rozilawati, Nordin Mohd Shukor (2020) Screening rice (Oryza sativa L.) genotypes for resistance against drought. Water Conservation and Management 4(2):78–82. https://doi.org/10.26480/wcm.02.2020.78.82
Lina, Yang Zenggang, **ong Gang, Liu Yong**, Hu Xuemin, Zhang Meikang, Qiu An Analytical Model of Page Dissemination for Efficient Big Data Transmission of C-ITS. IEEE Transactions on Intelligent Transportation Systems 1–10. https://doi.org/10.1109/TITS.2021.3134557
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This research was funded by the National Natural Science Foundation of China (U19A2061), the National Key R&D Program of China (2019YFC1710700), the Science and Technology Development Program of Jilin Province (20190301024NY), and the Jilin Provincial Development and Reform Commission Project (2020C005).
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Yu, H., Li, Z., Bi, C. et al. An effective deep learning method with multi-feature and attention mechanism for recognition of Chinese rice variety information. Multimed Tools Appl 81, 15725–15745 (2022). https://doi.org/10.1007/s11042-022-12458-2
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DOI: https://doi.org/10.1007/s11042-022-12458-2