1、Aljaifidroriguisroai -屠驚血 ReducingInformationVariationonTexts“(AgataSavaryandChristianJacquemin).WorkonourQAGroup一DFKI.IJJJJL/jJJJZdLJLyJJjZdJJbeXarkeywordmatchingisnotevethebestdocumentsfora.Forexample:,WhenwasAlbertEinsteiniwasbornin18791P，ofPhysicsAlbertEinsteinsrmany.一Born:14March1879inUlm,Wurit

2、temberg,Germany.一PhysicsnobelprizeAlbertEinsteinwasbornatUlm,inWurttemberg,Germany,onMarch14,1879.一Died18Apr1955(bom14Mar1879)German-Americanphysicist.Ihesameinformationcanbefoundinseveralways:-37刀刁勺刀仏/7刁迥4Kindsofvariatic-Graphic:ri14Me孑Morphological:”耳Syntactical:“German如-Semanic:AlbertEinste“Germa

3、n-Americanphysicist.訂Appropriateness:-Precision.79and1114Mar1879n.Physicsnobelpriz-Americanphysicistin隙$bornatUlmand一Economy. #LanguageModelsappliedtoInformationRetrieval(ChengxiangZhaiandJohnLafferty)Theprobwaprobabilq=q02qn=dd2.dfnp(qd)taqueryQwasgeneratedbybIbasedonadocument. # # # # #P(q|d)?Op(d

4、Iq)ap(qIcl)*p(d)Uni-grammodel:P(qd)=YP(qjd)/=!logp(qI)=工logP(QiId)i=l4esuseoftwoprobabilitesfor=z盹需罟（沖）沱（,d）0GWCl）i=Pu(qid)=adP(qiC) LguagModeling JJ.JJ刁嚴PSdatasparsenTheroleofsmoothingis:urate.henorvinformativew(em:AdiusttheMLEtocompensateenessordsintiequery.sretrievalperformancetotheumentLM?Goalof

5、theworK：，3暫為|r%-一Howsensitive.smoothingofadocumV.看亠一Howshouldbethemodeljandtheparameterschosen? theMLE: # # # #1-工A3ld）_vveV:c（vv；t/）0%=工P（wlC）vveV:c（iv；t/）0 #OSSmoothinq:tacklestheeffectofstatisticalvariabilitysmalltrliningsets.Cjiscountinq:therelativefrequenciesofseeneventsarediscounted;thegainedp

6、robabilitymassisthendistributedovertheunseenwords # # # # # -Bwwmdor；oodturinaidea:Estimatethee誦莎sn恰bytakingthecountsthetotal Theprobabilityofatermwithfreq,tfisgivenby:Nd=Totalnumberoftermsoccurredind.tfj(Zf+1)Numberoftermswithfrequencytfinadocument.酗站（g）S仙+1）s叫）NjTotalnumberoftermsoccurredind. # #

7、尊dsmethockinvolvesalinearh利舸nodewiththecollection #Methodscountinq:decreasetheprobabilityofse|rordsbjOCbstractingaconstantfromtheircounts. # # # # Md)/書廠仔JOeVajheideaistoadjusttheprobabilitiesaccordingtothequery.1J -p-(一p一+3一P一一p-(u一M)dn-PW(0好丄P(H+-p_)、：1一電a一一uleedgM)dr+(p-M)zd(I)(p_M)Qr一。乏上u一-poWsl

8、of16u-lunoos-pn_osq 2iininiiUTimesondisk4.5datat-Financiondisk5.osAngelesondisk5.-Disk4anddisk5minusCongressionalRecord.-TheTREC8webdata.Queries:一Topics351-400(TREClad-hoctask).一Topics401-450(TREC8adhocwebtask). #Number:384spacestationmoonDescription:Identifydocumentsthatdiscussthebuildingofaspacest

9、ationwiththeintentofcolonizingthemoonNarrative:Arelevantdocumentwilldiscussthepurposeofaspacestation,initiativestowardscolonizingthemoon,impedimentswhichthusfarhavethwartedsuchaprojectzplanscurrentlyunderwayorintheplanningstagesforsuchaventure;costcountriespreparedtomakeacommitmentofmen,resourcesAfa

10、cilitiesandmoneytoaccomplishsuchafeat. P刁臼心尼血Em也門9Number:414Description:countriesimportit?Narrative:Cuba,sugar,exportsHowmuchsugardoesCubaexportandwhichArelevantdocumentwillprovideinformationregardingCubassugartrade.Sugarproducticrnstatisticsarenotrelevantunlessexportsarementionedexplicitly.TREC h/t

11、ype:Interactrentversionofeachsetofqueries:Titleonly(2or3wordsAlongversion(Title+description一TwodiOptinmeansofthenorrir):hmethodbyigeprecision. +（1-人）臨（4Id）丄xjyPmiWid）IC）/PIC） # # 凸口山乂處泊沏gWKDirichletpriors:|Termweight:1C）丿 # # tUBAvAr.1df(CifffCrCC7frCrUllfVfAbsoute!discdependent:erforadocumentOjtfrf

12、lg:-CIsadocument-ghtofamwithaflatterlog1+c(qd)-66duP(qiCl 宜/韌勻潺渤JUConclusionsJeljnek-Mercer:ofterms.ling,thatis,lees-Theprecisionismuchmoresensitiveto入forlongqueriesthanfortitlequerieLongqueriesneedemphasisonthereI-|n阮webcollection,wassensitivetosmoothingfortitlequeriestoo.一Fortitlequeries!theretrie

13、valperformaneetendstobeoptimizedwhen入二0.1 iffirrr/r/r/fKKtKK|I44fISXK.Kamam.、丿rtitlequeries,especialluislarge,amqueries,oppositeoptimalvalueofutenM*J門刃OMsX/S門FuM屜!is # g/ConclusionsStop-List.FVPorterSteemer.elarge-spanipsintheianThepetormaneeofthen-grammodelhasreachedaplateau,P(d).丿r1rI!ri/irjrrIrlr

14、JrIZjritAnalysis奢,一Alowdimensionalrepresentationofthedata.PCAtriewhere,_onhowcl.ensionalrepr(Relationbetweenfeatures.esto!findalow?rankapproximation,thequalityoftheapproximationdependsclosethedataistollyinginasubspaceofthegivendimensionality.-SemanticInformationisextractedbymeansafttieSinaularValueD

15、ecomposition(SVD).DfUZVfLSIusesareductionofthefirstkcolumnsofU.da0()匕indelysis;is-lheeigenvectorsforasetofdocumentscanasconceptsdescribedbyalinear)noftermschoseninsuchawaynentsaredescribedasaccuratelyaspossibleusingonlyksuchconcepts.-Termsthatco-occurfrequentlywilltendtoaligninthesameeigenvectors #

16、# # # SVDieLAwjrfiprg/fWhathappensifwwsmoothing?irirShinroblem:thedocunentsIclassificationonthenewheterogeneous.Ifthedocumentsclassificationoftheambiguousthetodbelongdiversetopics,wordsinthenewspaceisthe -T一Smoothin!othetasteofthe)linguisticphenomena-一一一_lingmethodsaresimpleandefficient.evorovideaelegantwaytodealwiththedataMBessp誕m摻三)echooseac一But,theydonotmotbeh