Design of agricultural question answering information extraction method based on improved BILSTM algorithm
Abstract 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 ext...
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Nature Portfolio
2024-10-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-70534-z |
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| author | Ruipeng Tang Jianbu Yang Jianxun Tang Narendra Kumar Aridas Mohamad Sofian Abu Talip |
| author_facet | Ruipeng Tang Jianbu Yang Jianxun Tang Narendra Kumar Aridas Mohamad Sofian Abu Talip |
| author_sort | Ruipeng Tang |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-a0cab8cb98d94f098d21b94e65edcfa7 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a0cab8cb98d94f098d21b94e65edcfa72025-08-20T02:17:47ZengNature PortfolioScientific Reports2045-23222024-10-0114111210.1038/s41598-024-70534-zDesign of agricultural question answering information extraction method based on improved BILSTM algorithmRuipeng Tang0Jianbu Yang1Jianxun Tang2Narendra Kumar Aridas3Mohamad Sofian Abu Talip4Department of Electrical Engineering, Faculty of Engineering, University of MalayaFaculty of Languages and Linguistics, University of MalayaFaculty of Electronics and Electrical Engineering, Zhaoqing UniversityDepartment of Electrical Engineering, Faculty of Engineering, University of MalayaDepartment of Electrical Engineering, Faculty of Engineering, University of MalayaAbstract 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.https://doi.org/10.1038/s41598-024-70534-zInformation extractionQuestion and answer systemNatural language processingKnowledge graphAgricultural information recommendation |
| spellingShingle | Ruipeng Tang Jianbu Yang Jianxun Tang Narendra Kumar Aridas Mohamad Sofian Abu Talip Design of agricultural question answering information extraction method based on improved BILSTM algorithm Scientific Reports Information extraction Question and answer system Natural language processing Knowledge graph Agricultural information recommendation |
| title | Design of agricultural question answering information extraction method based on improved BILSTM algorithm |
| title_full | Design of agricultural question answering information extraction method based on improved BILSTM algorithm |
| title_fullStr | Design of agricultural question answering information extraction method based on improved BILSTM algorithm |
| title_full_unstemmed | Design of agricultural question answering information extraction method based on improved BILSTM algorithm |
| title_short | Design of agricultural question answering information extraction method based on improved BILSTM algorithm |
| title_sort | design of agricultural question answering information extraction method based on improved bilstm algorithm |
| topic | Information extraction Question and answer system Natural language processing Knowledge graph Agricultural information recommendation |
| url | https://doi.org/10.1038/s41598-024-70534-z |
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