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1、SIGIR2016TutorialPisaItalyJuly17,2016DeepLearningforInformationRetrievalHangLi&ZhengdongLuHuaweiNoah’sArkLabOutlineofTutorial•Introduction•Part1:BasicsofDeepLearning•Part2:FundamentalProblemsinDeepLearningforIR•Part3:ApplicationsofDeepLearningtoIR•SummaryOverviewofInformationRetri
2、evalContent:Documents,Images,Intent:RelationalTablesKeyWords,QuestionQueryInformationInformationandRetrievalSystemKnowledgeBaseRelevantResultKeyQuestions:HowtoRepresentIntentandContent,HowtoMatchIntentandContent•Ranking,indexing,etcarelessessential•InteractiveIRisnotparticularlyco
3、nsideredhereApproachinTraditionalIRDocument:Query:StarWars:EpisodeVIIstarwarstheforceawakensreviewsThreedecadesafterthedefeatoftheGalacticEmpire,anewthreatarises.qd11f(q,d)00q,df(q,d)VSM
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11、01•Representingqueryanddocumentastf-idfvectors•Calculatin
12、gcosinesimilaritybetweenthem•BM25,LM4IR,etccanbeconsideredasnon-linearvariantsApproachinModernIRQuery:Document:starwarstheforceawakensreviewsStarWars:EpisodeVIIThreedecadesafterthedefeatoftheGalacticqdEmpire,anewthreatarises.(starwars)vv(theforceawakens)q1f(q,d)d1(reviews)vvqmd
13、n•Conductingqueryanddocumentunderstanding•Representingqueryanddocumentasfeaturevectors•Calculatingmultiplematchingscoresbetweenqueryanddocument•Trainingrankerwithmatchingscoresasfeaturesusinglearningtorank“Easy”ProblemsinIR•Search–Matchingbetweenqueryanddocument•QuestionAnsweringf
14、romDocuments–Matchingbetweenquestionandanswer•Wellstudiedsofar•DeepLearningmaynothelpsomuch“Hard”ProblemsinIR•ImageRetrieval–Matchingbetweentextandimage–Notthesameastraditionalsetting•QuestionAnsweringfromKnowledgeBase–Complicatedmatchingbetweenquestionandfactinknowledgebase•Gener
15、ation-basedQuestionAnswering–Generatinganswertoquestionbasedonfactsinknowledgebase•Notwellstudiedsofar•DeepLearningcanmakeabigdealHardProblemsinIRQuestionAnsweringNameHeightWeightQ:HowtallisYaoMing?fromKnowledgeBaseYaoMing2.29m134kgLiuXiang1.89m85kg(Notagonimages)ImageRetrievalQ:A
16、dogcatchingaballGeneration-basedT