關(guān)系tri-training：利用無標記數(shù)據(jù)學習一階規(guī)則

關(guān)系tri-training：利用無標記數(shù)據(jù)學習一階規(guī)則Tri-Training:UsingUnlabeledDatatoLearnFirst-OrderRules

Abstract:Knowledgeengineeringhasemergedasasignificantbranchofartificialintelligence,howevermanualknowledgeengineeringislaborintensiveandtimeconsuming.Toreducetheeffortexpendedbyexperts,tri-trainingisintroducedtoautomaticallylearnfirst-orderrulesfromunlabeleddata.Tri-trainingemploysthreeclassifiersinaniterativeprocessthatusesunlabeleddataandself-trainingtogeneratelabelsthatarethenusedtocorrectmislabeledinstances.Ourpaperpresentsastudyoftri-trainingbycomparingitwithotherexistingtechniques.Experimentsusingtworealworlddatasetsshowthattri-trainingperformswellandconsistentlyyieldshigherpredictionaccuracythansingle-sourcelearning,baggingandboostingmodels.

Keywords:knowledgeengineering,artificialintelligence,tri-training,first-orderrules,unlabeleddata,self-training

Introduction:KnowledgeengineeringisanimportantbranchofArtificialIntelligence(AI)thatdealswithdevelopingandmanagingknowledge-basedsystems.Itistypicallyalaboriousandtimeconsumingtask.Traditionalknowledgeengineeringgenerallyrequiresmanualacquisitionofknowledgefromexpertswhichiscostlyandinefficient.Toreducethecostandtimeassociatedwithmanualknowledgeengineering,researchersareexploringwaystoautomaticallylearnfromunlabeleddata.

Oneofthemethodsproposedrecentlyistri-training.Tri-trainingisasemi-supervisedapproachthatusesunlabeleddatatolearnfirst-orderrules.Itutilizestheuncertainlabelingofthreebaseclassifiersandtheirconsensustolabeladditionalunlabeledinstances.Ititerativelybuildsthreeclassifiersonlabeledandpartiallylabeleddata.Italsoemploysself-trainingtoimprovetheaccuracyoftheclassifiers.

...(restofthepaperomitted)Tri-traininghasbeenusedinmanyapplicationssuchastextcategorization,NamedEntityRecognition(NER),andpredictinguserpreferences.Intextcategorization,tri-trainingcanbeusedtoclassifyshorttextsordocumentsintomultiplecategories.Itachievesahigherpredictionaccuracythansingle-sourcelearningandbaggingmodels.Tri-traininghasalsobeenappliedinNERtaskforautomaticallyidentifyingentitiesfromtext.Itimprovesthelabelingprocessbyprovidinglabelsfornoisydatawhichisknowntoreducethelabelingcostsignificantly.Anotherapplicationoftri-trainingistopredictusers'preferencesbylearningfromuserbehaviors.Thismethodhasbeenusedtobuildrecommendersystemsthatcanaccuratelypredictuser'spreferencesbasedonthehistoryoftheiractivities.

Overall,tri-trainingisapromisingapproachforsemi-supervisedlearningwithunlabeleddata.Experimentsusingtworealworlddatasetshaveshownthattri-trainingperformswellandconsistentlyyieldshigherpredictionaccuracythansingle-sourcelearning,baggingandboostingmodels.However,furtherresearchisneededtoimprovethescalabilityandrobustnessofthealgorithm.Thisincludesdevelopingalgorithmstoselectinformativelabelsandfeaturelearningtechniquestogeneraterepresentativefeatures.Inadditiontotheresearchmentionedabove,thereisaneedforfurtherexplorationontheuseofmoreadvancedmachinelearningtechniquesfortri-training.Forexample,deeplearningtechniquessuchasconvolutionalneuralnetworksandrecurrentneuralnetworkscanbeusedtoimprovetheaccuracyofthemodels.Anotherpromisingareaofresearchisthedevelopmentoftransferlearningalgorithmswhichallowknowledgeacquiredfromonedomaintobeappliedtoanotherdomain.Thiscanreducetheamountofdatarequiredforeffectivelearningandcanimprovetheaccuracyofthemodelsignificantly.

Additionally,currenttri-trainingalgorithmsrelyonafixedsetofinputfeatures.Tofurtherimprovetheaccuracyofthemodel,featureselectionalgorithmscanbeusedtoselectthemostinformativefeatures.Thiswillreducethenumberofirrelevantfeaturesandcanimprovetheaccuracyofthemodel.Finally,animportantareaofresearchisthedevelopmentofalgorithmsthatcanautomaticallydeterminethebestcombinationoflabelsandfeatureswhichwouldenableamoreefficientknowledgeacquisitionprocess.

Inconclusion,tri-trainingisapowerfulsemi-supervisedapproachthatcanbeusedtolearnfromunlabeleddata.Withfurtherresearchandimprovements,ithasthepotentialtoreducethecostandtimeassociatedwithknowledgeengineering.Tri-trainingisaneffectivesemi-supervisedlearningapproachwhichutilizesunlabeleddatatogeneratefirst-orderrules.Ithasbeenusedforavarietyoftaskssuchastextcategorization,NamedEntityRecognition(NER),anduserpreferenceprediction.Experimentshavedemonstratedthattri-trainingcansignificantlyoutperformsingle-sourcelearning,baggingandboostingmodels.

Furtherresearchisneededtoimprovethescalabilityandrobustnessofthealgorithm.Somepotentialareasofexplorationincludetheuseofdeeplearningtechniques,transferlearningalgorithmsandfeatureselectionalgorithms.Additionally,algorithmstoautomaticallydeterminethebestcombinationoflabelsandfeaturescouldfurtherimprovetheaccuracy