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{"title":"从银行业大数据中提取名称实体识别的工具","authors":"C. Saju, S. Ravimaran","doi":"10.4018/IJWSR.2020040102","DOIUrl":null,"url":null,"abstract":"Generally,theInternetistheglobalsystemofinterconnectedcomputernetworks,connectingmillions ofcomputersaswellaspeople,andthusgeneratesamassivequantityofinformationonadailybasis. Thisleadstoextractingthenecessaryinformationusinginformationfiltering(IF)inseveraldomains. Inourimplementation,thenamedentityrecognition(NER)techniqueisemployedtoautomatically extract valuable data from the unstructured natural language texts. As several works has been outlinedindetectingnamedentities,plentyofverydifferentNERtoolsexistforseveraldomains. However,NERremainsagiantchallengesotosolvethisproblemweproposedanovelframeworkby combiningthreeefficientclassifiers.Thisarticleproposesathree-layeredneuralnetworkapproach withconditionalrandomfield(CRF),thePachinkoallocationmodel(PAM),andtheAdaptiveNeuroFuzzyInferenceSystem(ANFIS)fordetectingnamedentitiesinthreesteps.First,aclassifierbased onCRFisemployedtotraintheinputfile.Second,PAMisemployedtoboostthepreviousoutput createdbyCRFtoenhancethelabelannotation.Third,theANFIScapturesthedeepfeaturesofthe informationbyitselffromthepre-trainedinformationtoattainaccuratepredictions.Experimental resultsshowthatthelearnedmodelyieldsabankingdomainwitharecallrateof92%,aprecision rateof95%andF-measureof92%byimplementingitintheRPlatform. KEyWoRD Adaptive Neuro-Fuzzy Inference System, Conditional Random Field, Information Filtering, Named Entity Recognition, Natural Language Processing, Pachinko Allocation Model","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"47 1","pages":"18-39"},"PeriodicalIF":0.8000,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"A Tool to Extract Name Entity Recognition From Big Data in Banking Sectors\",\"authors\":\"C. Saju, S. Ravimaran\",\"doi\":\"10.4018/IJWSR.2020040102\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Generally,theInternetistheglobalsystemofinterconnectedcomputernetworks,connectingmillions ofcomputersaswellaspeople,andthusgeneratesamassivequantityofinformationonadailybasis. Thisleadstoextractingthenecessaryinformationusinginformationfiltering(IF)inseveraldomains. Inourimplementation,thenamedentityrecognition(NER)techniqueisemployedtoautomatically extract valuable data from the unstructured natural language texts. As several works has been outlinedindetectingnamedentities,plentyofverydifferentNERtoolsexistforseveraldomains. However,NERremainsagiantchallengesotosolvethisproblemweproposedanovelframeworkby combiningthreeefficientclassifiers.Thisarticleproposesathree-layeredneuralnetworkapproach withconditionalrandomfield(CRF),thePachinkoallocationmodel(PAM),andtheAdaptiveNeuroFuzzyInferenceSystem(ANFIS)fordetectingnamedentitiesinthreesteps.First,aclassifierbased onCRFisemployedtotraintheinputfile.Second,PAMisemployedtoboostthepreviousoutput createdbyCRFtoenhancethelabelannotation.Third,theANFIScapturesthedeepfeaturesofthe informationbyitselffromthepre-trainedinformationtoattainaccuratepredictions.Experimental resultsshowthatthelearnedmodelyieldsabankingdomainwitharecallrateof92%,aprecision rateof95%andF-measureof92%byimplementingitintheRPlatform. KEyWoRD Adaptive Neuro-Fuzzy Inference System, Conditional Random Field, Information Filtering, Named Entity Recognition, Natural Language Processing, Pachinko Allocation Model\",\"PeriodicalId\":54936,\"journal\":{\"name\":\"International Journal of Web Services Research\",\"volume\":\"47 1\",\"pages\":\"18-39\"},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2020-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Web Services Research\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.4018/IJWSR.2020040102\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Web Services Research","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.4018/IJWSR.2020040102","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
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A Tool to Extract Name Entity Recognition From Big Data in Banking Sectors
Generally,theInternetistheglobalsystemofinterconnectedcomputernetworks,connectingmillions ofcomputersaswellaspeople,andthusgeneratesamassivequantityofinformationonadailybasis. Thisleadstoextractingthenecessaryinformationusinginformationfiltering(IF)inseveraldomains. Inourimplementation,thenamedentityrecognition(NER)techniqueisemployedtoautomatically extract valuable data from the unstructured natural language texts. As several works has been outlinedindetectingnamedentities,plentyofverydifferentNERtoolsexistforseveraldomains. However,NERremainsagiantchallengesotosolvethisproblemweproposedanovelframeworkby combiningthreeefficientclassifiers.Thisarticleproposesathree-layeredneuralnetworkapproach withconditionalrandomfield(CRF),thePachinkoallocationmodel(PAM),andtheAdaptiveNeuroFuzzyInferenceSystem(ANFIS)fordetectingnamedentitiesinthreesteps.First,aclassifierbased onCRFisemployedtotraintheinputfile.Second,PAMisemployedtoboostthepreviousoutput createdbyCRFtoenhancethelabelannotation.Third,theANFIScapturesthedeepfeaturesofthe informationbyitselffromthepre-trainedinformationtoattainaccuratepredictions.Experimental resultsshowthatthelearnedmodelyieldsabankingdomainwitharecallrateof92%,aprecision rateof95%andF-measureof92%byimplementingitintheRPlatform. KEyWoRD Adaptive Neuro-Fuzzy Inference System, Conditional Random Field, Information Filtering, Named Entity Recognition, Natural Language Processing, Pachinko Allocation Model
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