LimitedDomainKnowledgeQuestionAnsweringSystemBasedonBI-LSTM-CRF

doi:10.3969/j.issn.1006-2475.2018.07.011

Abstract

Abstract: Withthedevelopmentofopenfieldquestion-answeringsystemandtheurgentneedoftheintegrationofmechanicalindustryandartificialintelligence,itisnecessarytoestablishaknowledgebasequestionansweringsystemformachineryfield.Basedonmechanicalindustrydataandnaturallanguageprocessingtechniques,anetworkmodelbasedonconditionalrandomfieldandlongandshorttermmemoryneuralnetworkisproposedtoimprovetheinformationextractionperformanceandtoestablishaknowledgebasequestion-answeringsysteminmachineryindustry.Throughthecomparativeanalysisofexperimentaldata,themodelhasachievedgoodresults.

Key words: CRF, LSTM, informationextraction, questionanswering

CLC Number:

TP391.1

CHENGShu-dong,HUYing. LimitedDomainKnowledgeQuestionAnsweringSystemBasedonBI-LSTM-CRF[J]. Computer and Modernization, 2018, 0(07): 53-.

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