Design of agricultural question answering information extraction method based on improved LSTM algorithm

Design of agricultural question answering information extraction method based on improved LSTM algorithm | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Design of agricultural question answering information extraction method based on improved LSTM algorithm Ruipeng Tang, Narendra Kumar Aridas, Mohamad Sofian Abu Talip This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4370119/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version 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 IM-BILSTM (Improved Bidirectional Long Short-Term Memory) algorithm is designed. Firstly, it uses Python's Scrapy crawler framework to obtain the imformation 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 IM-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, so as to Improve 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. Physical sciences/Mathematics and computing/Information technology Physical sciences/Mathematics and computing/Scientific data Physical sciences/Mathematics and computing/Statistics information extraction question and answer system natural language processing knowledge graph agricultural in-formation recommendation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 23 May, 2024 Reviews received at journal 23 May, 2024 Reviewers agreed at journal 19 May, 2024 Reviews received at journal 17 May, 2024 Reviewers agreed at journal 15 May, 2024 Reviewers invited by journal 15 May, 2024 Editor assigned by journal 15 May, 2024 Editor invited by journal 08 May, 2024 Submission checks completed at journal 07 May, 2024 First submitted to journal 04 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4370119","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":301320613,"identity":"99e4d837-9ec0-4d59-96ce-50106f189e89","order_by":0,"name":"Ruipeng Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIie3QMUvDQBTA8RceXJeTrBdCv8PBQUQUxG/So5Ap7hkELwTOpdjVwa/gJGROCLTLgWsgg5auLhLopFbjIAXJUTeR+y8PHvx43AG4XH8wf5Rdda/pFn3w1PeW2UgwqxdADY4CtS/hTRzDgUaflztbK4EyiZ6YIaF4qPOOXoC8U1i1FAqpBoSnzCHnKRVRI3VIFyCLkkyPKbSDBL0ZZxPDplHjaUxIT2gU2ghBylml+eX9vMq75L0n/sZKKCFxkOkJcpAqPNdfV4iVMIq1AFMi69/yds1EURNxdMtbMUROH1fZGtIS/fly/XKzORkXy3zVPKfteIj8uPr5I/3kcLYv2bn/a+FyuVz/tQ+3IFtKDiRPQQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Malaya","correspondingAuthor":true,"prefix":"","firstName":"Ruipeng","middleName":"","lastName":"Tang","suffix":""},{"id":301320614,"identity":"5ac11ace-3448-4d3e-8335-e4f9a671ec55","order_by":1,"name":"Narendra Kumar Aridas","email":"","orcid":"","institution":"University of Malaya","correspondingAuthor":false,"prefix":"","firstName":"Narendra","middleName":"Kumar","lastName":"Aridas","suffix":""},{"id":301320615,"identity":"b500da0b-0c30-42fb-a7f6-345dfd500bc8","order_by":2,"name":"Mohamad Sofian Abu Talip","email":"","orcid":"","institution":"University of Malaya","correspondingAuthor":false,"prefix":"","firstName":"Mohamad","middleName":"Sofian Abu","lastName":"Talip","suffix":""}],"badges":[],"createdAt":"2024-05-05 03:09:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4370119/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4370119/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-70534-z","type":"published","date":"2024-10-18T15:57:35+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":67149253,"identity":"54ddab33-3d91-4331-b372-e094ed242138","added_by":"auto","created_at":"2024-10-21 16:12:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":595430,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscriptnewest.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4370119/v1_covered_d4391aed-349a-45b1-b5f5-86c2b9fe46e0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Design of agricultural question answering information extraction method based on improved LSTM algorithm","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"information extraction, question and answer system, natural language processing, knowledge graph, agricultural in-formation recommendation","lastPublishedDoi":"10.21203/rs.3.rs-4370119/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4370119/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"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 IM-BILSTM (Improved Bidirectional Long Short-Term Memory) algorithm is designed. Firstly, it uses Python's Scrapy crawler framework to obtain the imformation 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 IM-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, so as to Improve 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.","manuscriptTitle":"Design of agricultural question answering information extraction method based on improved LSTM algorithm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-15 15:35:57","doi":"10.21203/rs.3.rs-4370119/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-23T18:30:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-23T05:05:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"80824458959171367721078691391529903997","date":"2024-05-19T08:16:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-17T11:47:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"199533358608820404859003381373917067310","date":"2024-05-15T10:47:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-15T10:41:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-15T10:40:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-08T10:56:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-07T09:31:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-05-05T03:06:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3b8ddb8c-894a-432a-8f0c-5909d5ae6f7f","owner":[],"postedDate":"May 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":31795078,"name":"Physical sciences/Mathematics and computing/Information technology"},{"id":31795079,"name":"Physical sciences/Mathematics and computing/Scientific data"},{"id":31795080,"name":"Physical sciences/Mathematics and computing/Statistics"}],"tags":[],"updatedAt":"2024-10-21T16:07:23+00:00","versionOfRecord":{"articleIdentity":"rs-4370119","link":"https://doi.org/10.1038/s41598-024-70534-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-10-18 15:57:35","publishedOnDateReadable":"October 18th, 2024"},"versionCreatedAt":"2024-05-15 15:35:57","video":"","vorDoi":"10.1038/s41598-024-70534-z","vorDoiUrl":"https://doi.org/10.1038/s41598-024-70534-z","workflowStages":[]},"version":"v1","identity":"rs-4370119","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4370119","identity":"rs-4370119","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}