Treffer: Design of agricultural question answering information extraction method based on improved BILSTM algorithm.
Title:
Design of agricultural question answering information extraction method based on improved BILSTM algorithm.
Authors:
Tang R; Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia. 22057874@siswa.um.edu.my., Yang J; Faculty of Languages and Linguistics, University of Malaya, 50603, Kuala Lumpur, Malaysia., Tang J; Faculty of Electronics and Electrical Engineering, Zhaoqing University, No. 55, Zhaoqing City, Guangdong Province, China., Aridas NK; Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia., Talip MSA; Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia.
Source:
Scientific reports [Sci Rep] 2024 Oct 18; Vol. 14 (1), pp. 24444. Date of Electronic Publication: 2024 Oct 18.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: PubMed not MEDLINE; MEDLINE
Imprint Name(s):
Original Publication: London : Nature Publishing Group, copyright 2011-
References:
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Saraswat, M. & Srishti,. Leveraging genre classification with RNN for book recommendation. Int. J. Inf. Technol. 14(7), 3751–3756 (2022).
Zhou, X., Li, Y. & Liang, W. CNN-RNN based intelligent recommendation for online medical pre-diagnosis support. IEEE/ACM Trans. Comput. Biol. Bioinform. 18(3), 912–921 (2020). (PMID: 10.1109/TCBB.2020.2994780)
El Bourakadi, D., Ramadan, H., Yahyaouy, A. & Boumhidi, J. A novel solar power prediction model based on stacked BiLSTM deep learning and improved extreme learning machine. Int. J. Inf. Technol. 15(2), 587–594 (2023).
Singla, P., Duhan, M. & Saroha, S. An ensemble method to forecast 24-h ahead solar irradiance using wavelet decomposition and BiLSTM deep learning network. Earth Sci. Inform. 15(1), 291–306 (2022). (PMID: 10.1007/s12145-021-00723-134804244)
He, X., Feng, J., Sun, F., Yan, M., Qian, J., Dai, W., & Wang, H. A Biomedical trigger word identification method based on BERT and CRF. In International Conference on Web Information Systems and Applications 393–402 (Springer, 2022).
Feng, X., Liu, Q., & Liu, X. Intelligent question answering system based on knowledge graph. In 2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys) 1515–1520 (IEEE, 2021).
Valim Bandeira, M., Ferreira de Souza Móta, L. M. F., & Behr, A. Decision-making in agribusiness based on artificial intelligence. Braz. J. Manag./Revista de Administração da UFSM 15 (2022).
Yang, T., Mei, Y., Xu, L., Yu, H., & Chen, Y. Application of question answering systems for intelligent agriculture production and sustainable management: A review. Resources, Conservation and Recycling 204, 107497 (2024).
Lin, Y., Ji, H., Huang, F., & Wu, L. A joint neural model for information extraction with global features. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics 7999–8009 (2020).
Luan, Y., Wadden, D., He, L., Shah, A., Ostendorf, M., & Hajishirzi, H. A general framework for information extraction using dynamic span graphs. Preprint at arXiv:1904.03296 . (2019).
Nie, Y., Chen, H., & Bansal, M. Combining fact extraction and verification with neural semantic matching networks. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, No. 01, 6859–6866 (2019).
Yang, Y., Agarwal, O., Tar, C., Wallace, B. C., & Nenkova, A. Predicting annotation difficulty to improve task routing and model performance for biomedical information extraction. Preprint at http://arxiv.org/abs/1905.07791 (2019).
Sahu, S. K., Christopoulou, F., Miwa, M., & Ananiadou, S. Inter-sentence relation extraction with document-level graph convolutional neural network. http://arxiv.org/abs/1906.04684 (2019).
Jiang, J., Wang, A., & Aizawa, A. Attention-based relational graph convolutional network for target-oriented opinion words extraction. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume 1986–1997 (2021).
Guo, X. et al. Chinese agricultural diseases and pests named entity recognition with multi-scale local context features and self-attention mechanism. Comput. Electron. Agric. 179, 105830 (2020). (PMID: 10.1016/j.compag.2020.105830)
Adnan, K. & Akbar, R. An analytical study of information extraction from unstructured and multidimensional big data. J. Big Data 6(1), 1–38 (2019). (PMID: 10.1186/s40537-019-0254-8)
Chen, G., Sui, X., & Kamruzzaman, M. Agricultural remote sensing image cultivated land extraction technology based on deep learning. Technology 9(10) (2019).
Padilla, W. R., García, J. & Molina, J. M. Knowledge extraction and improved data fusion for sales prediction in local agricultural markets. Sensors 19(2), 286 (2019). (PMID: 10.3390/s19020286306420436358854)
Fan, Y. Design and implementation of distributed crawler system based on Scrapy. In IOP Conference Series: Earth and Environmental Science vol. 108, no. 4, p. 042086 (IOP Publishing, 2018).
Wei, W. et al. Accurate and fast URL phishing detector: a convolutional neural network approach. Comput. Netw. 178, 107275 (2020). (PMID: 10.1016/j.comnet.2020.107275)
Bourhis, P., Reutter, J. L. & Vrgoč, D. JSON: Data model and query languages. Inf. Syst. 89, 101478 (2020). (PMID: 10.1016/j.is.2019.101478)
Ariyadasa, S., Fernando, S. & Fernando, S. Combining long-term recurrent convolutional and graph convolutional networks to detect phishing sites using URL and HTML. IEEE Access 10, 82355–82375 (2022). (PMID: 10.1109/ACCESS.2022.3196018)
Brahmia, Z., Hamrouni, H. & Bouaziz, R. XML data manipulation in conventional and temporal XML databases: A survey. Comput. Sci. Rev. 36, 100231 (2020). (PMID: 10.1016/j.cosrev.2020.100231)
Kumar, A., Dabas, V. & Hooda, P. Text classification algorithms for mining unstructured data: A SWOT analysis. Int. J. Inf. Technol. 12(4), 1159–1169 (2020).
Titouan, V., Courty, N., Tavenard, R., & Flamary, R. Optimal transport for structured data with application on graphs. In International Conference on Machine Learning 6275–6284 (PMLR. 2019).
Kalnoor, G. & Gowrishankar, S. A model for intrusion detection system using hidden Markov and variational Bayesian model for IoT based wireless sensor network. Int. J. Inf. Technol. 14(4), 2021–2033 (2022).
Zheng, C., Deng, N., Cui, R., & Lin, H. Terminology extraction of new energy vehicle patent texts based on BERT-BILSTM-CRF. In International Conference on Emerging Internetworking, Data & Web Technologies 190–202 (Springer, 2023).
Zhang, D. C., Li, Z., Zhang, Y. & Lin, W. H. Noun metaphor recognition based on transformer and BERT. Data Anal. Knowl. Disc. 4, 9 (2020).
Di Gennaro, G., Buonanno, A., & Palmieri, F. A. Considerations about learning Word2Vec. J. Supercomput. 1–16 (2021).
Saraswat, M. & Srishti,. Leveraging genre classification with RNN for book recommendation. Int. J. Inf. Technol. 14(7), 3751–3756 (2022).
Zhou, X., Li, Y. & Liang, W. CNN-RNN based intelligent recommendation for online medical pre-diagnosis support. IEEE/ACM Trans. Comput. Biol. Bioinform. 18(3), 912–921 (2020). (PMID: 10.1109/TCBB.2020.2994780)
El Bourakadi, D., Ramadan, H., Yahyaouy, A. & Boumhidi, J. A novel solar power prediction model based on stacked BiLSTM deep learning and improved extreme learning machine. Int. J. Inf. Technol. 15(2), 587–594 (2023).
Singla, P., Duhan, M. & Saroha, S. An ensemble method to forecast 24-h ahead solar irradiance using wavelet decomposition and BiLSTM deep learning network. Earth Sci. Inform. 15(1), 291–306 (2022). (PMID: 10.1007/s12145-021-00723-134804244)
He, X., Feng, J., Sun, F., Yan, M., Qian, J., Dai, W., & Wang, H. A Biomedical trigger word identification method based on BERT and CRF. In International Conference on Web Information Systems and Applications 393–402 (Springer, 2022).
Contributed Indexing:
Keywords: Agricultural information recommendation; Information extraction; Knowledge graph; Natural language processing; Question and answer system
Entry Date(s):
Date Created: 20241018 Latest Revision: 20241021
Update Code:
20250114
PubMed Central ID:
PMC11489568
DOI:
10.1038/s41598-024-70534-z
PMID:
39424853
Database:
MEDLINE
Weitere Informationen
With the rapid growth of the agricultural information and the need for data analysis, how to accurately extract useful information from massive data has become an urgent first step in agricultural data mining and application. In this study, an agricultural question-answering information extraction method based on the BE-BILSTM (Improved Bidirectional Long Short-Term Memory) algorithm is designed. Firstly, it uses Python's Scrapy crawler framework to obtain the information of soil types, crop diseases and pests, and agricultural trade information, and remove abnormal values. Secondly, the information extraction converts the semi-structured data by using entity extraction methods. Thirdly, the BERT (Bidirectional Encoder Representations from Transformers) algorithm is introduced to improve the performance of the BILSTM algorithm. After comparing with the BERT-CRF (Conditional Random Field) and BILSTM algorithm, the result shows that the BE-BILSTM algorithm has better information extraction performance than the other two algorithms. This study improves the accuracy of the agricultural information recommendation system from the perspective of information extraction. Compared with other work that is done from the perspective of recommendation algorithm optimization, it is more innovative; it helps to understand the semantics and contextual relationships in agricultural question and answer, which improves the accuracy of agricultural information recommendation systems. By gaining a deeper understanding of farmers' needs and interests, the system can better recommend relevant and practical information.
(© 2024. The Author(s).)