Welch Junghoo Cho and Walter Chang mjwelchcsuclaedu chocsuclaedu wachangadobecom Abstract With the proliferation of online distribution methods for videos content owners require easier and more e64256ective methods for monetization through advertis ID: 1529 Download Pdf
NOTTCSTR20011 Arc Segmentation in Engineering Drawings Dave Elliman First released March 2001 Copyright 2001 Dave Elliman In an attempt to ensure goodquality printouts of our technical reports from the supplied PDF files we process to PDF using Acr
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OUTLINE. ●Information Technology (IT): An Historical perspective. ●Storage and retrieval of information. ●Advantages of IT. ●Evolution of computers. P.O. Ogunmolu: Computer Science Department, AAUA, Nigeria.
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Computer Science Laboratory 333 Ravenswood Ave Menlo Park CA 94025 650 3266200 Facsimile 650 8592844 brPage 3br Abstract To illustrate some of the power and convenience of its speci64257cation language and the orem prover we use the PVS formal veri6
Smart-phones in USA. Dr. Jeyakesavan Veerasamy. Director of Senior Design projects & Sr. Lecturer. University of Texas at Dallas. Dr. V. Jeyakesavan: . Academia. , . Industry & . Personal. Dad .
Welch Junghoo Cho and Walter Chang mjwelchcsuclaedu chocsuclaedu wachangadobecom Abstract With the proliferation of online distribution methods for videos content owners require easier and more e64256ective methods for monetization through advertis
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UCLAComputerScienceDepartmentTechnicalReport#100025 Theremainderofthispaperisorganizedasfollows.WediscussrelatedworkinSection2andprovideanoverviewofourgoalsandapproachinSection3.Section4describesoursystemforextractingkeywordsfromcontent-basedtextsources,andinSection5wepresentmethodsforexpandingthosetermstoaddressvocabularymismatchproblemsbetweenthesourcekeywordsandthosechosenbyadvertisers.WeevaluatethekeywordsgeneratedbythesemethodsforvariousvideotypesandsourcesoftextinSection6,anddiscussourobservations,conclusions,andfutureworkinSection7.2RelatedWorkSponsoredsearch,oradvertisingdisplayedalongsidethesearchresultsofauser-suppliedkeywordquery,typicallyinvolvesacomplexcombinationofadvertisersbiddingonkeywords,reviewofadvertisementsforrelevance,andanauctionprocesstoplaceadsalongsidesearchresults.See[2]foranoverviewofsponsoredsearch.Indisplayorcontent-matchadvertising,however,explicitkeywordsforthecontentarenotpro-vided.Inonlineadvertisingitisimportanttodisplayadsrelevanttoapage'scontent[24].Withoutuser-suppliedkeywords,researchershaveinvestigatednumerouskeywordidenticationtechniquesandapproachestomatchadvertisementswiththecontentofWebpages.Ontologiesortaxonomieshavebeenusedincombinationwithfeatureidenticationforsemanticapproachestomatchingadvertisementswithcontent[5,7].Ontologiesareoftendomain-specicandtedioustoconstruct,andtheindividualtextelementsfromscriptsordialogareoftenterse,makingtheuseofclassicationtechniquesorontologiesmoreerrorprone.Yih,Goodman,andCarvalho[25]establishseveralfeaturesofWebpagesandquerylogs,suchasfrequency,textualcharacteristics(e.g.capitalization),andstructuralcuesforidentifyingadvertisingkeywords.Ribeiro-Netoetal.[18]proposestrategiesformatchingthetextofaWebpagewithtext-basedadvertisementsinaknownadinventory.Theyaddressthevocabularyimpedanceproblembyrepresentingapagewithconceptsfromitsnearest(mostsimilar)neighbors.Ravietal.[17]proposeatwophasegenerativemodelforidentifyingrelevantadvertisingkeywordsforagivenWebpage.TheyuseapopularmachinetranslationmethodtolearnaprobabilisticsetofkeywordmappingsfromatrainingcorpusofWebpagesassociatedwithadsandadvertiserchosenkeywords.TermweightsareassignedbasedonHTMLfeatures.Abigramlanguagemodeltrainedonsearchqueriesisusedtohelprankthegeneratedcandidatekeywords.Findingrelatedbut\lessobvious"(andthereforelessexpensive)keywordsfromanadvertiserspointofview[11,1]hasbeenaddressedaswell.Ourworkdiersfromtheseproblemsinseveralways.OurtextsourcesareplaintextwhichlackexplicitstructuralcuessuchasHTMLmarkup.Wethereforemustresorttostatisticalmethodsforrankingandselectingkeywordsfromthesourcetext.Severaloftheabovetechniquesalsorequiretaggedtrainingdata,languageanddomain-specicontologies,orpre-constructedpoolsofavailableadvertisements.Ourmethodsarelanguage-independentandunsupervised,notrequiringanytrainingdata.Relatedtermidenticationisawellresearchedproblemintheinformationretrievaldomain,wheretaskssuchasqueryrewritingorexpansionarewidelystudied.Voorhees[23]describedtheuseoflexicalrelationshipscontainedinWordNetforqueryexpansion.Buckleyetal.[6]notedthatrelatedtermswilltypicallyco-occurnon-randomlyindocumentsrelevanttoaquery.Morerecently,SahamiandHeilman[19]useasimilarnotiontocomputethesemanticsimilarityofshorttextsnippetsusingWebsearchresultsasanopaquecontext.OurworkfollowsalongtheselinesbyusingWebsearchresultstodiscoverrelatedco-occurringterms.WealsousetheimplicitsemanticrelationshipscapturedinthehyperlinkedstructureofWikipediatoidentifyrelatedterms.Hauptmannsummarizesmany\lessonslearned"regardingspeechrecognitionaccuracyandtheeectsofworderrorrateoninformationretrievalprecision[9].Inparticulartheirresearchshowsthatthebestsystemsachieveworderrorratesaround0.15underidealconditions(suchasin-studioanchorsforbroadcastnews),andthatretrievalperformancedegradesrelativelygracefullywithrespecttoperfecttexttranscriptsuntilworderrorratesapproach0.40.Whiletheirworkonretrievalisorthogonaltoourfocusofadvertisingkeywordselection,weconsiderthesendingsaswecompareourresultsbetweenperfecttexttranscripts(closedcaptioningtracks)andspeechtranscripts.Keywordidenticationformultimediaoftenutilizes,inpart,attributesextractedfromimagesaspartofalargerfeaturespaceformachinelearning.VelivelliandHuang[22]predicttagsforvideosbasedonimagefeaturesandspeechtranscripts.Usingacollectionofspeechtranscripts,theyperformaPLSI-basedclusteringtoformktopicthemes.Eachclusterisusedtogenerateaunigramlanguagemodeli,andasceneisassumedtobeamixtureofthesemodelsandanunderlyingbasemodel.Tagsarethenpredictedusingacombinationofshotfeaturesandkeywordco-occurrencebasedonaconstructedtrainingset.2 UCLAComputerScienceDepartmentTechnicalReport#100025 Figure1:ScriptProcessingWork owthisstage,ourgoalistoincreasethelikelihoodofmatchinganadvertiser'skeywordswhileminimizingdeclineinrelevancyoftheadswhenmatchesdooccur.ThisrelatedtermminingprocessisdescribedinSection5.Inbothsteps,keywordsareidentiedandrankedwithoutconsultinganinventoryofadsoradvertisersuppliedkeywords.4ProcessingSourceTextIntherststageofprocessing,weanalyzetheformatandcomplexitiesofvideo-basedtextsources,suchasscripts,anddescribemethodsoftextanalysisbasedontraditionalstatisticalanalysisandgenerativemodels.Inthisworkweconsiderthreesourcesoftextdataforavideo:MovieScript-ascriptorscreenplayisadocumentthatoutlinesallofthevisual,audio,behavioral,andspokenelementsrequiredtotellastory.Sincelmproductionisahighlycollaborativemedium,thedirector,cast,editors,andproductioncrewwillusevariousformsofthescripttointerprettheunderlyingstoryduringtheproductionlmingprocess.Numerousindividualsareinvolvedinthemakingofalm,thereforeascriptmustconformtospecicstandardsandconventionsthatallinvolvedpartiesunderstandandthuswilluseaspecicformatwithrespecttothelayout,margins,notation,andotherproductionconventions.Thisdocumentisintendedtostructureallofthescriptelementsusedinascreenplay.ClosedCaptioning(CC)track-adocumentwhichcontainsaseriesoftimecodesandtextofthespokendialog.Eachtimecodeindicateswhenandforwhatdurationthecorrespondingtextappearsonscreen.Closedcaptioningtrackslackadditionalcues,suchasvisualinformationorindicatorsofthecurrentspeaker.Speech-To-Text(STT)-isaprocessbywhichaudiodatacontainingdialogornarrativecontentisautomaticallyconvertedtoatexttranscription.Theoutputtypicallyconsistsofaseriesofwords,eachwithanassociatedtimecodeandduration.Thesourceaudiomaybeofpoorqualityorcontainnon-speechsoundssuchasmusicorsoundeectartifacts,whichgenerallycontributetotranscriptionerrors.TranscriptionqualityistypicallymeasuredbytheoverallWordErrorRate(WER).AfrequentgoalofSTTsystemsistoreducetheimpactofahighWER,thougherrorratesonheterogeneouscontentistypicallyquitehigh.Figure1outlinestheprocessingwork owforacompletemoviescript,whichincludesnon-speechelementssuchassceneheadingsandactiondescriptions.Wewilldescribeeachofthesestepsnext.Notethatthework owislargelythesameforclosedcaptioningtracksandspeechtranscripts,whichcanbeformattedto\look"likeascreenplay.Inthosecases,script-specicprocessingstepsaresimplyomitted.4.1ScriptParsingTelevisionandmoviescriptsarefrequentlywritteninplaintextandfollowaconventional\screenplay"formatwhichallowshumanreaderstoeasilydierentiateandinferthepropersemanticsfordierentscriptelements,suchasdialogorsceneheadings.Forexample,sceneheadingsaretypicallywrittenonasinglelineinallcapitalletters,beginningwithINTorEXTtodenotewhetherthesettingisinteriororexterior,andendingwithanindicatoroftimeofdaysuchasMORNINGorNIGHT.Figure2showsabriefsnippetofatypicalscript.Understandingthesemanticsofatextelementishelpfulwhenprocessingit.Forexample,characternamesappearfrequentlyinascriptpriortoeachoftheirlinesofdialog,thoughwegenerallyndthemtobeapoorchoiceforadvertisingkeywords.Weaddamachine-readablehierarchicalstructure4 UCLAComputerScienceDepartmentTechnicalReport#100025 Figure2:ExampleScriptSnippetandsemanticstoeachtextsegmentofascriptusinganitestatemachinebasedparserderivedfromconventionalscreenplaywritingrules.Thisisdepictedasstep(1)inFigure1.Moviescriptdocumentsareconvertedintoastructuredandtaggedrepresentationwhereallscriptelements(sceneheadings,actiondescriptions,dialoglines,etc.)aresystematicallyextracted,tagged,andrecordedasobjectsintoaspecializeddocumentobjectmodel(DOM)forsubsequentprocessing.AllobjectswithintheDOM(e.g.,entiresentencestaggedbytheircorrespondingtypeandscriptsection)arethenprocessedusingbothstatisticalmethodstoidentifykeywordsofinterest,andanaturallanguageprocessing(NLP)enginethatidentiesandtagsthenounitemsidentiedineachsentence.Theseextractedandtaggednounelementsarethencombinedwithtime-alignmentinformationandrecordedintoametadatarepository.Wedescribethisalignmentprocessnext.4.2Speech-to-Text(STT)ProcessingSTTtranscriptscontaintimecodeinformationthatplaysanimportantroleinassociatingscriptkeywordstospecicpointsintimeinthevideocontent.Inthissectionofthework ow,avideooraudiolethatcontainsspokendialogthatcorrespondstothedialogsectionsoftheinputscriptisreadandprocessedusingaSpeech-to-Textenginethatgeneratesatranscriptionofthespokendialog,shownas(2)inFigure1.Forthisprocess,wealsoperformanimportantoptimization.Automaticspeechrecognitionenginestypicallyincorporateaknownvocabularyandprobabilisticmodelsofspeech(oftenbasedonwordN-grams).Whenthedialogdataisavailablefromascript,weconstructacustomlanguagemodeltobiasthetranscriptionenginetowardstheexpectedvocabularyandwordsequences,whichhelpstoincreasethetranscriptionaccuracy.4.3ScriptandSTTTranscriptAlignmentAtthisstage,wehavetaggedandstructuredscriptdata(withoutanytimeinformation)fromstep(1),andanoisy,relativelyinaccurateSTTtranscriptwithveryprecisetimecodeinformationfromstep(2).Tomakeuseofthekeywordsandconceptsgeneratedbythelaterprocessingsteps,thescriptdatamustbetime-alignedwiththeSTTdata.Thisisaccomplishedinstep(3)byusingtheLevenshteinWordEditDistance[12]algorithmtondthebestwordalignmentbetweenscriptdialogandtheSTTtranscript.Theresultofthisphaseofprocessingisatime-alignedsourcescriptthatcanassociatescriptactionanddialogkeywordswithprecisepointsintimewithinthevideocontent.Thisdataisstoredintoametadatarepository.4.4StatisticalGenerationofKeywordTermsInthenalstepforasourcetext(script,CC,ortranscript),thetime-codedtextelementsfromthemetadatarepositoryareusedtobuildasuxwordN-gramtreethatisprunedbyN-gramtermfrequencytodiscoverthemostdominantterms,basedinlargepartontheworkofChimandDeng[8].Thisisshownas(5)inFigure1.BeforeN-gramtermgeneration,weperformedaone-timeprocessofselectingastopwordvocabularyspecictothedomainofmoviescripts.Usingfrequencystatisticscomputedfromalargecorpusofscripts,wemanuallyidentiedasetofstopwordsfromthemostfrequentlyoccurringterms.DuringN-gramtermgeneration,thefollowingstepsarefollowed:1.Corpusstopwordsareremovedfromthesourcetext.2.AnN-gramtermtreewithsequencesuptolengthN=4iscreatedbycollectingandcountingN-gramoccurrencesfromthescript.5 UCLAComputerScienceDepartmentTechnicalReport#100025 3.TheresultingsuxtreeisthenprunedbytraversingthetreetocollectandrankthetopmostMfrequentterms.Inourexperiments,weselectthemostfrequentM=20keywords.4.5GenerativeModelsForNoisyDataThestatisticalN-grammethodsworkwellwhenkeywordsandphrasesarerepeatedmultipletimes.Whilethisisoftenthecaseforlongerorwell-formedtextinput,shortornoisytextoftenresultsinthemajorityof(non-stopword)keywordsonlybeingmentionedonce.Withthistypeofinput,statisticalmodelsareunabletodecipherwhichkeywordsaremostimportant.Tobetterhandleshortornoisytextinput,weuseakeywordselectionmethodbasedongenerativetopicmodeling.Inthismodel,weassumethatavideocomprisesasmallnumberofhiddentopics,whichcanberepresentedaskeywordprobabilities,andthatavideo'stextisgeneratedfromsomedistributionoverthosetopics.Thehighlyprobablekeywordsinthosetopicsarelikelytobemostrepresentativeofthevideocontent.WeuseLatentDirichletAllocation(LDA)[4]tolearnthetopicsandcorrespondingtopic-keywordprobabilitydistributionfromtheinputtext.Wethencombinethesetopicstoformarankedkeywordlist.4.5.1GeneratingTopicsTodiscovertheunderlyingtopicsinavideo,wesegmenttheinputtextintosentencesandperformtopicmodelingwithLDA.Theresultingtopic-termdistributionisaKxVmatrix,whereKisthenumberoftopics,Visthesizeoftheinputvocabulary,and[i][j]istheprobabilityofkeywordjintopici.Weformanorderedlistofkeywordskiforeachtopic,sortedbytheirprobabilityin~[i].ThisresultsinKrankedlistsofkeywords,onepertopic,whichmustthenbemergedintoasinglelisttoselectthetopM.WhilesimplyselectingthetopM Kkeywordsfromeachtopicisoneoption,wedescribeamoregeneralsolutionformergingmultiplerankedlistswhenwediscussourapproachtondingrelatedkeywords.ThismethodisdescribedinSection5.3.4.6Statistical-GenerativeHybridMethodTheLDAmodellearnskeywordprobabilitiesfortermswhichareseparatedbywhitespace.Whenpos-sible,however,itispreferabletoidentifymulti-termkeywordsforadvertising.Forexample,thephrase\relationaldatabase"ismorespecicthaneitheroftheindividualwords\relational"or\database",andthushashighervaluetoadvertisers.Tohelpidentifythesemulti-tokenkeywordsinshortornoisytextsources,weuseahybridofstatisticalandgenerativetechniques.WerstprocessthesourcetextusingtheN-grammethodtoidentifyanysignicantmulti-tokenkeywords.Wetheneditthesourcetextbyremovingthewhitespacebetweenthetermsofthesemulti-tokenphrasessotheyappearasasingletoken.Thismodiedsourcetextisthenprocessedusingthegenerativemodel.4.7FilteringtheKeywordsWeapplytwolters,whenpossible,toremovefrequentlyoccurringwordswhichareoftennotusefulinthecontextofmatchingadvertisements.Fromallinputsources,keywordsmatchingalistofEnglishprofanityareremoved.Wealsondthatmaincharacternamesareoftenamongstthetopkeywords,butgenerallydonotretrieverelevantadvertisements.Whengivenacompletescript,weremovecharacternamesfromthekeywordlistusingadictionaryconstructedduringtheparsingandtaggingstage.Forclosedcaptioningandspeechtranscripts,however,thesenamesareunknownandthusmaystillappearinthetopkeywords.Thisismorecommonforclosedcaptioningthanspeechtranscripts,however,aspropernamesarelesslikelytobecorrectlytranscribedbytheSTTengine.Atthispointthemostdominant(possiblymulti-term)keywordswhichoccurinthesourcetext,alongwithassociatedtimecodeinformation,havebeenidentiedandcanbesuggestedasadvertisingkeywordsrelevanttoaparticulartimepointofavideo.AsRibeiro-Netoetal.[18]describe,however,thekeywordschosendirectlyfromasourceandthekeywordsbidonbyadvertisersmaysueravocabularyimpedanceproblem.Inthenextsectionwedescribenoveltermminingtechniqueswhichcanprovidearicher,morecompletesetofrelevantadvertisingkeywords.6 UCLAComputerScienceDepartmentTechnicalReport#100025 Figure3:RelatedTermsFromSearchResultsAftertheselteringsteps,weconstructavectorspacemodelMforthissmallcorpusof\documents"relevanttoT.BasedonthepopularTF-IDF[20]termweighting,wecomputethecorpusfrequency(CF)andinverse-document-frequency(IDF)weightforeachterminM,andrankthekeywordsaccordingtotheirCF*IDFscore.Thisstepisshowninthework owas(3)inFigure3,producingthenallistofrankedrelatedkeywordsfromsearchresults.5.2MiningwithWikipediaTheseconddatasourceweanalyzeforrelatedtermsisWikipedia,anextensiveknowledgebasewithover3.1millionEnglisharticlesavailableatthetimeofthiswriting.WhereasintheWebcorpuswefocusedonsearchresultsinresponsetoaquery,withWikipediawedirectourattentiontohyperlinks.WithinthetextofaWikipediaarticle,numerousinter-wikilinkspointtootherWikipediapages,whichallowsustomodelWikipediaasadirectedgraphG=fV;Eg.WeconstructtheWikipediagraphwherenodesVrepresentpagesinthemainarticlenamespace,andedgesEdenoteinter-wikilinksbetweenthosepages.Whenbuildingthegraph,twoarticletypesinthemainnamespaceareprocessedspecially.Forambiguoustermssuchas\coach",adisambiguationpageinWikipedialiststheavailablearticlesfordierentsensesoftheterm.Thesepagesserveprimarilyasnavigationalaidesforusers,ratherthanconveyingasemanticrelationshipbetweenterms,andwethere-foreexcludetheminthegraph.Thesecondcategoryofpagesweprocessspeciallyareredirectionpages,whichprovideatranslationforalternateormisspelledwords,inconsistentcapitalization,acronyms,andsoon,intoacanonicalform.InourWikipediagraph,anarticleandallofthepageswhichredirecttoitaremergedintoasinglenode.Weusethelinkstructureofthegraphtobothidentifyandrankcandidaterelatedterms.Thesestepsaredescribedindetailinthefollowingsections.5.2.1IdentifyingCandidateRelatedTermsWithoutclearlydeneddirectedlinksbetweenindividualtermsintheWebcorpus,theapproachesusingWebsearchresultsdescribedabovedependontheassumptionsthatdocumentsretrievedbythesearchenginearerelevanttotheinputterms,andthatothertagsorkeywordsforthosepagesarepotentiallyrelated.Thatis,werelyonco-occurrencebasedmeasurestoidentifywhichtermsaremostlikelyrelated.WithWikipedia,however,wehaveanexplicitlinkstructurebetweenarticleswhichcanbeusedasanindicatorofrelatedness.Werequiretherelatednessbetweentwoarticlenodesaandbtobeasymmetricrelationship:aisrelatedtobifandonlyifbisrelatedtoa.TranslatingthisrequirementtotheWikipediagraphisrelativelystraightforward.Toidentifycandi-daterelatedtermsfortermT,werstlocatetheWikipediapagewithTasthetitle.3GiventhenodetforT,weidentifyanynodesinthegraphwhichformadirectcyclewithtascandidaterelatedterms.Thatis,weselectthesubsetofnodesNVsuchthat:8n2V;n2N=)ft;ng^fn;tg2E(1)Figure4showsasimpleexample,wherefortermt,termsn1andn2arecandidaterelatedterms,butXandYarenot.5.2.2RankingCandidateTermsAgivensetofcandidaterelatedtermsmaybequitelarge.Wenowlookathowtorankthecandidateterms.Tobeagoodsuggestionasanadvertisingkeyword,atermshouldberelativelypopular.Whilewecouldmeasurepopularitythroughexternalsources,suchasquerylogfrequency,wechosetoutilizethegraphstructureofWikipedia.Weapproximatetherelativeimportanceoftermsbycomputing 3WecanrelaxthisrequirementandsearchthetextofWikipediaarticlestoidentifythetoppageorpagesforanyparticularinputterm,albeitatalikelyreductioninqualityofthegeneratedrelatedterms.8 UCLAComputerScienceDepartmentTechnicalReport#100025 SR(camera) WP(camera) Combined SR(advertising) WP(advertising) Combined digitalcamera photography digitalcamera product internet internet lens pornography photography marketing newspaper product canon visualarts canon advertiser videogame marketing nikon photograph nikon business americanfootball newspaper zoom digitalcamera pornography campaign magazine advertiser lmcamera photojournalism lens advertisingagency worldwideweb magazine digitalslr photographiclm digitalphotography internet marketing advertisingagency megapixels aperture photograph consumer mtv publicrelations digitalphotography canon aperture job blog google compact photographiclens shutterspeed newspaper publicbroadcastingservice billboard camcorder aerialphotography visualarts agency massmedia videogame slrcamera holography exposure publicrelations google publicity lense single-lensre excamera viewnder company brand productplacement digitalslrcamera focallength moviecamera service broadcasting graphicdesign olympus nikon cameraphone budget musicvideo promotion Table2:ExampleRelatedTermsbyMethod6.1EvaluationDesignWeidentiedthetop20keywordsfromeachavailabletextsourceusingboththestatisticalandhybridapproachesdescribedinSection4.ForeachofthesekeywordsweusetherelatedtermminingtechniquesofSection5toidentifythetop10relatedterms.Thesekeywordswerethenevaluatedwithausersurvey.Forthetopicmodelingphaseofthehybridtechnique,wesetthenumberoftopicsK=5withtheLDAparameters=0:3and=0:1.Userswereshownavideoclip,typicallyaround3minutesinlength,andasetofkeywords.Tokeepthesizeofthekeywordsetmanageable,weshow5ofthetop20keywordsforeachmethodfromeachavailabletextsource,and1ofthetop10relatedtermsforeachofthosekeywords,allchosenandorderedatrandom.Userswereaskedtomakeabinaryassessmentontherelevanceofeachdisplayedkeyword.ForthenewsandeducationalvideosandtheamateurclipsavailableonYouTube,usersareshownthecompletevideo.Forfulllengthlms,usersareshownthetheatricaltrailerandaskedtomakejudgementsbasedonthetrailerandtheirpriorknowledgeofthemovie.Over23peopleparticipatedinthesurvey(personallyidentiableinformationwasnotrequired),withaminimumof9andanaverageof13usersevaluatingeachvideo.6.2EvaluationMetricsWeevaluatethekeywordsgeneratedbyourmethodsusingfourmetrics.Theaveragerelevancyofthekeywordsdisplayedtouserswecalltheprecision.Multipleusersviewingthesamesetofkeywordsmaynotcompletelyagreeonwhichkeywordsarerelevant.Wethereforecomputethepotentialofasource,whichmeasuresthefractionofthekeywordsjudgedrelevantbyatleastoneuser.Moreformally,wedenetheprecisionandpotentialoftextsourceSas:Precision(S)=1 iXijKi(S)\Rij jKi(S)jPotential(S)=jR(S)j jK(S)jRiisthesetofkeywordsjudgedrelevantinevaluationiandKi(S)arethekeywordsdisplayedtotheuserforevaluationiwhichcomefromsourceS.K(S)arethekeywordsfromsourceSdisplayedinatleastoneevaluation,andR(S)arethekeywordsfromsourceSjudgedrelevantbyatleastoneuser,denedas:R(S)=[iKi(S)\RiK(S)=[iKi(S)Theothermetricswedeneareappealandpopularity,whichserveasindicatorsofhowpertinentthekeywordsaretoadvertisers.Appealestimatesthelikelihoodthatakeyworddeemedrelevanttothecontentwillalsobemeaningfultoanadvertiser.Popularitymeasurestheaveragenumberofadvertisersinterestedinarelevantkeyword.WedenetheappealandpopularityofasourceSas:Appeal(S)=jR(S)\Aj jR(S)j10 UCLAComputerScienceDepartmentTechnicalReport#100025 VideoType Precision Potential Statistical Hybrid Statistical Hybrid StudioFilms 0.268 0.252 0.479 0.480 News/Educational 0.442 0.473 0.548 0.717 UserGenerated 0.268 0.368 0.390 0.473 Table4:PrecisionandPotentialforSTT VideoType WER Statistical Hybrid StudioFilms 0.857 0.723 0.690 News/Educational 0.406 0.731 0.961 Table5:RelativePrecisionandWordErrorRateHauptmann'sworkindicatesthatspeech-to-textworderrorratesunder0.4resultinretrievalper-formancecomperabletoaperfecttranscript[9].Atthe0.4threshold,relativeretrievalprecisionisapproximately80%.Wecomputetheaverageworderrorrateforstudiolmsandnews/educationalvideos(usingthedefault\general"languagemodelsforourSTTengine),andcomparetherelativepre-cisionofSTTwithrespecttoclosedcaptioningforthestatisticalandhybridmethods,showninTable5.Usergeneratedvideosarenotincludedbecauseno\correct"transcriptsareavailableforthecontent.Asexpected,theaverageworderrorratesfornewsandeducationalvideosaresubstantiallylower,thoughstillaround0.4.Forthistypeofcontent,therelativeprecisionofSTTis96%oftheclosedcaptioning.Forthehigherworderrorrateoflmswecanstillachieveover70%averagerelativeprecision.Theseresultsfurthersupportuseofthestatisticalselectionmethodsonlongertextinputsandthegenerativemethodsonshortertext,andsuggestthatspeechtranscriptsalonemaybesucienttondmeaningfuladvertisingkeywordsforvideossuchasnewsbroadcasts.6.5PrecisionandPotentialofRelatedTermsWenextlookattheprecisionandpotentialoftherelatedterms.Table6showstheprecisionandpotentialscoresforthetop10relatedtermsfromboththestatistical(S-Related)andhybrid(H-Related)methods.TheseresultsaremostlyconsistentwithTable3,withthemostpreciseinputsource(closedcaptioning)producingthemostrelevantrelatedkeywords.Foreachmethodandsource,theprecisionandpotentialofthesourcekeywordsarehigherthantherelatedterms.InourexperimentswerandomlyselectedfromthetopN=10relatedtermsforeachsourcekeyword.Wenowinvestigatehowtheaverageprecisionoftherelatedtermsisaectedaswevarythisrangefor1N10.Figure5plotstheprecisionoftherelatedkeywordsforeachtextsourceusingthestatisticalselectionmethod.Forclosedcaptioningthetop2relatedtermsgivethehighestprecision,whichislowerthantheprecisionofthesourcetermsbutsignicantlyhigher(p=0:003)thanchoosingfromthetop10.Bothscriptandspeechtranscriptinputsshowanincreaseinprecisionwhenselectingfromthetop3-6terms.Whiletheprecisionisagainlowerthanthesourcekeywords,thereisnoticeableimprovementbetweenselectingfromthetopN=6andN=10forbothscript(p=0:06)andSTT(p=0:03)input.Thisresultsuggeststhatthenumberofrelatedtermstoconsidertoachievethemaximumoverallprecisiondependsontheinputtexttype,withhigherprecisioninputlikeclosedcaptioningachievingitsbestprecisionwithasmallernumberrelatedtermsthanscriptsorspeechtranscripts.Resultsforthehybridselectionmethodexhibitsimilarbehavior.Anotherfactortoconsiderwhenevaluatingtheprecisionoftherelatedkeywordsistherelevancyofthesourcetermbeingexpanded.Anirrelevantsourcetermislesslikelytoresultinrelevantrelated Source Precision Potential S-Related H-Related S-Related H-Related Script 0.254 0.215 0.253 0.222 CC 0.260 0.221 0.262 0.221 STT 0.208 0.186 0.200 0.191 Table6:PrecisionandPotentialofRelatedTerms12 UCLAComputerScienceDepartmentTechnicalReport#100025 Source Statistical S-Related Hybrid H-Related Script 0.726 0.788 0.607 0.792 CC 0.578 0.785 0.543 0.796 STT 0.681 0.827 0.594 0.820 Table7:AppealofKeywordsbySource Source Statistical S-Related Hybrid H-Related Script 3.59 3.96 3.00 4.18 CC 2.11 3.81 2.00 3.77 STT 2.54 4.39 2.56 4.30 Table8:PopularityofKeywordsbySourceForbothappealandpopularitywenoticethat,whileclosedcaptioningwasgenerallyconsideredthemostprecisesourceofkeywords,italsoproducestheleastmeaningfulkeywordsforadvertisers.Thismaybearesultofcharacternamesappearingintheclosedcaptioningkeywords,whichwenotedearlierarelteredoutfromscriptinputtextandarelesslikelytoretrieverelevantads.Finally,welookcloseratthepopularityofkeywordsforspeechtranscripts.Table9comparesthepopularityforsourceandrelatedkeywordsforvariousvideotypes.Inallcasestherelatedkeywordshavehigherpopularitythanthesourcekeywordsbyastatisticallysignicantmargin.Italsoshowsthatnewsandeducationalcontentcontainslesspopularkeywordsforadvertisers.6.7Precision-PopularityTradeosTheresultsabovedemonstratethat,whenrelevant,relatedkeywordsaresignicantlymoreattractivetoadvertisersthansourcekeywords.Theoverallprecisionoftherelatedterms,however,islowerthansourceterms.Weexploretheinherenttradeobetweenkeywordrelevanceandpopularitybycomputingaprecision-weightedpopularitymetric:PWP(S)=Pk2K(S)AkP(S;k) jK(S)j(4)WhereP(S;k)istheprecisionofkeywordkfromsourceS,denedas:P(S;k)=Pijfkg\Ri(S)j Pijfkg\Ki(S)jTable10showstheprecision-weightedpopularityforthestatisticalmethodforeachtextsourceusingthetop5relatedkeywordsfromeachsourcekeyword.Theresultssuggeststhatforscriptinput,theminorimprovementinpopularityofrelatedkeywords(showninTable8)maynotosetthedecreaseinprecision.Forspeechtranscriptinput,however,thereappearstobesomebenetfromrelatedterms.WeexamineSTTinputfurtherinTable11,whichshowsthatoverall,evenwiththedropinprecision,relatedkeywordsarebenecialtoadvertisersfornewsandusergeneratedvideoswhenonlyspeechtranscriptsareavailable.Althoughtherelatedkeywordsforstudiolmspeechtranscriptshavehigherpopularitythansourcekeywords(Table9),therelativeincreaseisnoticeablylowerthanforCCorSTT,andtheresultingprecision-weightedpopularitydoesnotoerimprovement. 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