Báo cáo khoa học: "Extracting Key Semantic Terms from Chinese Speech Query for Web Searches" ppt

ExtractingKeySemanticTermsfromChineseSpeechQueryforWeb Searches  GangWANG  NationalUniversityof Singapore wanggang_sh@hotmail.com   Tat-SengCHUA  NationalUniversityofSinga- pore chuats@comp.nus.edu.sg  Yong-ChengWANG  ShanghaiJiaoTongUniver- sity,China,200030 ycwang@mail.sjtu.edu.cn Abstract This paper discusses the challenges and pro- poses a solution to performing information re- trievalontheWebusingChinesenaturallanguage speech query. The main contribution of this re- searchisindevisingadivide-and-conquerstrategy toalleviatethespeech recognition errors. It uses thequerymodeltofacilitatetheextractionofmain coresemanticstring(CSS)fromtheChinesenatu- rallanguagespeechquery.ItthenbreakstheCSS into basic components corresponding to phrases, and uses a multi-tier strategy to map the basic components to known phrases inorderto further eliminatetheerrors.Theresultingsystemhasbeen foundtobeeffective. 1 Introduction Weareentering aninformation era, where infor- mationhasbecomeoneofthemajorresourcesin ourdailyactivities.Withitswidespreadadoption, Internethasbecomethelargestinformationwealth for all to share.Currently, most (Chinese)search engines can only support term-based information retrieval,wheretheusersarerequiredtoenterthe queriesdirectlythroughkeyboardsinfrontofthe computer. However, there is a large segment of populationinChinaandtherestoftheworldwho areilliterateanddonothavetheskillstousethe computer.Theyarethusunabletotakeadvantage ofthevastamountoffreelyavailableinformation. Since almost every person can speak and under- standspokenlanguage,theresearchon“(Chinese) natural language speech query retrieval” would enableaveragepersonstoaccessinformationusing thecurrentsearchengineswithouttheneedtolearn specialcomputerskillsortraining.Theycansim- ply access the search engine using common de- vices that they are familiar with such as the telephone,PDAandsoon. Inordertoimplementaspeech-basedinforma- tion retrieval system, one of the most important challengesishowtoobtainthecorrectqueryterms fromthespokennaturallanguagequerythatcon- veythemainsemanticsofthequery.Thisrequires theintegrationofnaturallanguagequeryprocess- ingandspeechrecognitionresearch. Naturallanguagequeryprocessinghasbeenan activeareaofresearchfor many yearsandmany techniques have been developed (Jacobs and Rau1993;Kupie,1993; Strzalkowski,1999;Yuet al,1999).Mostofthesetechniques,however,focus onlyonwrittenlanguage,withfewdevotedtothe studyofspokenlanguagequeryprocessing. Speech recognition involves the conversion of acousticspeechsignalstoastreamoftext.Because ofthecomplexityofhumanvocaltract,thespeech signalsbeingobservedaredifferent,evenformul- tipleutterancesofthesamesequenceofwordsby thesameperson(Leeetal1996).Furthermore,the speechsignalscanbeinfluencedbythedifferences across different speakers, dialects, transmission distortions, and speaking environments. These have contributed to the noise and variability of speechsignals.Asoneofthemainsourcesofer- rorsinChinesespeechrecognitioncomefromsub- stitution (Wang 2002; Zhou 1997), in which a wrongbutsimilarsoundingtermisusedinplaceof thecorrectterm,confusionmatrixhasbeenusedto recordconfusedsoundpairsinanattempttoelimi- nate this error. Confusion matrix has been em- ployed effectively in spoken document retrieval (Singhaletal,1999andSrinivasanetal2000)and tominimizespeechrecognitionerrors(Shenetal, 1998). However, when such method is used di- rectlytocorrectspeechrecognitionerrors,ittends tobringin too many irrelevantterms(Ng2000). Becauseimportant terms in a longdocumentare oftenrepeatedseveraltimes,thereisagoodchance thatsuchtermswillbecorrectlyrecognizedatleast oncebyaspeechrecognitionenginewithareason- ablelevelof wordrecognitionrate.Manyspoken documentretrieval(SDR)systemstookadvantage ofthisfactinreducingthespeechrecognitionand matchingerrors(Mengetal2001;Wangetal2001; Chen et al2001). Incontrastto SDR,very little work has been done on Chinese spoken query processing(SQP),whichistheuseofspokenque- riestoretrievaltextualdocuments.Moreover,spo- kenqueriesinSQPtendtobeveryshortwithfew repeatedterms. In this paper, we aim to integrate the spoken languageandnaturallanguageresearchtoprocess spokenquerieswithspeechrecognitionerrors.The maincontributionofthisresearchisindevisinga divide-and-conquerstrategytoalleviatethespeech recognition errors. It first employs the Chinese query model to isolate the Core Semantic String (CSS) that conveys the semantics of the spoken query. It then breaks the CSS into basic compo- nentscorrespondingtophrases,andusesamulti- tierstrategytomapthebasiccomponentstoknown phrasesinadictionaryinordertofurthereliminate theerrors. Intherestofthispaper,anoverviewofthepro- posedapproachisintroducedinSection2.Section 3describesthequerymodel,whileSection4out- lines the use of multi-tier approach to eliminate errorsinCSS.Section5discussestheexperimental setup and results.Finally,Section 6 contains our concludingremarks. 2 Overviewoftheproposedapproach Therearemanychallengesinsupportingsurfingof Webbyspeechqueries.Oneofthemainchallenges isthatthecurrentspeechrecognitiontechnologyis notverygood,especiallyforaverageusersthatdo nothaveanyspeechtrainings.Forsuchunlimited user group, the speech recognition engine could achieveanaccuracyoflessthan50%.Becauseof this,thekeyphraseswederived fromthespeech querycouldbeinerrorormissingthemainseman- ticofthequeryaltogether.This wouldaffectthe effectivenessoftheresultingsystemtremendously. Giventhespeech-to-textoutputwitherrors,the keyissueisonhowtoanalyzethequeryinorderto grasptheCoreSemanticString(CSS)asaccurately as possible. CSS is defined as the key term se- quenceinthequerythatconveysthemainseman- tics of the query. For example, given the query: “ ”(Pleasetell metheinformationonhowtheU.S.separatesthe most-favored-nation status from human rights is- sueinchina).TheCSSinthequeryisunderlined. WecansegmenttheCSSintoseveralbasiccom- ponentsthatcorrespondtokey concepts suchas:  (U.S.),  (China),  (human rightsissue), (themost-favored-nation status)and (separate). Because of the difficulty in handling speech recognitionerrorsinvolvingmultiple segmentsof CSSs,welimitourresearchtoqueriesthatcontain onlyoneCSSstring.However,weallowaCSSto includemultiplebasiccomponentsasdepicted in theaboveexample.Thisisreasonableasmostque- riesposedbytheusersontheWebtendtobeshort withonlyafewcharacters(Pu2000). Thus the accurate extraction of CSS and its separation into basic components is essential to alleviatethespeechrecognitionerrors.Firstofall, isolatingCSSfromtherestofspeechenablesusto ignoreerrorsinotherpartsofspeech,suchasthe greetingsandpoliteremarks,whichhavenoeffects ontheoutcomeofthequery.Second,byseparating theCSSintobasiccomponents,wecanlimitthe propagationoferrors,andemploythesetofknown phrasesinthedomaintohelpcorrecttheerrorsin thesecomponentsseparately.        Figure1:Overviewoftheproposedapproach To achieve this, weprocess the query in three mainstagesasillustratedinFigure1.First,given theuser’soralquery,thesystemusesaspeechrec- ognitionenginetoconvertthespeechtotext.Sec- ond, we analyze the query using a query model (QM) to extract CSS from the query with mini- mumerrors.QMdefinesthestructuresandsome of the standard phrases used in typical queries. Third,wedividetheCSSintobasiccomponents, andemployamulti-tierapproachtomatchtheba- QM  Confusionmatrix  PhraseDictionary  Multi-Tier mapping Basic Components  Speech Query  CSS  sic components to the nearest known phrases in ordertocorrectthespeechrecognitionerrors.The aimhereistoimproverecallwithoutexcessivelost in precision. The resulting key components are thenusedasquerytostandardsearchengine. The following sections describe the details of ourapproach. 3 QueryModel(QM) Querymodel (QM) is used to analyzethe query and extract the core semantic string (CSS) that containsthemainsemanticofthequery.Thereare twomaincomponentsforaquerymodel.Thefirst isquery componentdictionary,which isa set of phrasesthathascertainsemanticfunctions,suchas the polite remarks, prepositions, time etc. The othercomponentisthequerystructure,whichde- finesasequenceofacceptablesemanticallytagged tokens, such as “Begin, Core Semantic String, QuestionPhrase, and End”.Each querystructure alsoincludesitsoccurrenceprobabilitywithinthe query corpus. Table 2 gives some examples of querystructures. 3.1QueryModelGeneration Inordertocomeupwithasetofgeneralizedquery structures, we use a query log of typical queries posedbyusers.Thequerylogconsistsof557que- ries,collectedfromtwenty-eighthumansubjectsat the Shanghai Jiao Tong University (Ying 2002). Eachsubjectisaskedtopose20separatequeriesto retrievegeneralinformationfromtheWeb. After analyzing the queries, we derive a query modelcomprising51querystructuresandasetof query components. For each query structure, we compute its probability of occurrence, which is used to determine the more likely structure con- tainingCSSincasetherearemultipleCSSsfound. Aspartoftheanalysisofthequerylog,weclassify thequerycomponentsintotenclasses,aslistedin Table1.Thesetenclassesarecalledsemantictags. Theycanbefurtherdividedintotwomaincatego- ries:theclosedclassandopenclass.Closedclasses are those that have relatively fixed word lists. Theseincludequestionphrases,quantifiers,polite remarks, prepositions, time and commonly used verb and subject-verb phrases. Wecollectallthe phrasesbelongingtoclosedclassesfromthequery logandstoretheminthequerycomponentdiction- ary.TheopenclassistheCSS,which wedonot knowinadvance.CSStypicallyincludesperson’s names,eventsandcountry’snamesetc. Table1:DefinitionandExamplesofSemantictags SemTag  Nameoftag Example 1. Verb-Object Phrase  give   (me) 2. QuestionPhrase (isthere) 3. QuestionField (news), (report) 4. Quantifier (some) 5. VerbPhrase (find) collect  6. PoliteRemark (pleasehelp me) 7. Preposition (about), (about) 8. Subject-Verb phrase (I) (want) 9. CoreSemantic String 9.11  (9.11event) 10. Time (today) Table2:ExamplesofQueryStructure  1  Q1:0,2,7,9,3,0:0.0025,  9.11   2793 IsthereanyinformationonSeptember11?  2  Q2:0,1,7,9,3,0:0.01     1793 GivemesomeinformationaboutBenladen. Giventhesetofsamplequeries,aheuristicrule- basedapproachisusedtoanalyzethequeries,and break them into basic components with assigned semantictagsbymatchingthewordslistedinTa- ble 1. Any sequences of words or phrases not foundintheclosedclassaretaggedasCSS(with Semantic Tag 9). We can thus derive the query structuresoftheformgiveninTable2. 3.2ModelingofQueryStructureasFSA Duetospeechrecognitionerrors,wedonotexpect thequerycomponentsandhencethequerystruc- turetoberecognizedcorrectly. Instead,weparse thequerystructure inordertoisolateandextract CSS.Tofacilitatethis,weemploytheFiniteState Automata(FSA)tomodelthequerystructure.FSA modelstheexpectedsequencesoftokensintypical queries andannotate the semantictags,including CSS.AFSA isdefinedforeach of the51query structures.AnexampleofFSAisgiveninFigure2. BecauseCSSisanopenset,wedonotknowits contentinadvance.Instead,weusethefollowing tworulestodeterminethecandidatesforCSS:(a) itisan unknownstring not present intheQuery Component Dictionary; and (b) its length is not lessthantwo,astheaveragelengthofconceptsin Chineseisgreaterthanone(Wang1992). At each stage of parsing the query using FSA (Hobbsetal1997),weneedtomakedecisionon which state to proceedand how to handleunex- pected tokens in the query. Thus at each stage, FSAneedstoperformthreefunctions: a) Gotofunction:Itmapsapairconsistingofa stateandaninputsymbolintoanewstateor thefailstate.WeuseG(N,X)=N’todefine thegotofunctionfromStateNtoStateN’, giventheoccurrenceoftokenX. b) Fail function: It is consulted whenever the gotofunctionreportsafailurewhenencoun- teringanunexpectedtoken.Weusef(N)=N’ torepresentthefailfunction. c) Output function: In the FSA, certain states aredesignatedasoutputstates,which indi- cate that a sequence of tokens has been found and are tagged with the appropriate semantictag. To construct a goto function, we begin with a graph consisting of one vertex which represents State0.WethenentereachtokenXintothegraph byaddingadirectedpathtothegraphthatbegins atthestartstate.Newverticesandedgesareadded tothegraph sothat therewill be, startingat the startstate,apathinthegraphthatspellsoutthe tokenX.ThetokenXisaddedtotheoutputfunc- tionofthestateatwhichthepathterminates. Forexample,supposethatourQueryComponent Dictionary consists of seven phrases as follows: “  (please help me);  (some);  (about); (news); (collect); (tell me);  (what do youhave)”. Adding these tokensintothegraphwillresultinaFSAasshown inFigure2.ThepathfromState0toState3spells outthephrase“ (Pleasehelpme)”,andon completion of this path, we associate its output withsemantictag6.Similarly,theoutputof“  (some)” is associated with State 5, and semantic tag4,andsoon. Wenowuseanexampletoillustratetheprocess of parsing the query. Suppose the user issues a speechquery:” ” (please help me to collect some information about Bin Laden).However, the resultofspeech recognition witherrors is: ”  (please)  (help) (me) (receive) (send) (some) (about) (half) (pull) (light) (of)  (news)”. Note that there are 4 mis-recognized characterswhichareunderlined.  Note:indicatesthesemantictag. Figure2:FSAforpartofQueryComponentDictionary TheFSAbeginswithState0.Whenthesystem encountersthesequenceofcharacters (please) (help) (me),thestatechangesfrom0to1,2 andeventuallyto3.AtState3,thesystemrecog- nizes a polite remark phrase and output a token withsemantictag6. Next,thesystemmeetsthecharacter (receive), itwilltransittoState10,becauseofg(0, )=10. Whenthesystemseesthenextcharacter (send), which does not have a corresponding transition rule, the goto function reports a failure. Because thelengthofthestringis2andthestringisnotin theQueryComponentDictionary,thesemantictag 9isassignedtotoken” ”accordingtothedefi- nitionofCSS. By repeating the aboveprocess, we obtain the followingresult:       694793 HerethesemantictagsareasdefinedinTable1. Itisnotedthatbecauseofspeechrecognitionerrors, thesystem detected twoCSSs,andboth ofthem containspeechrecognitionerrors. 3.3CSSExtractionbyQueryModel Giventhat we mayfind multiple CSSs, the next stageistoanalyzetheCSSsfoundalongwiththeir surroundingcontextinordertodeterminethemost probableCSS.Theapproachisbasedontheprem- isethatchoosingthebestsenseforaninputvector amountstochoosingthemostprobablesensegiven that vector. The input vector i has three compo- nents:leftcontext(L i ),theCSSitself(CSS i ),and rightcontext(R i ).Theprobabilityofsuchastruc- tureoccurringintheQueryModelisasfollows:  = = n j jiji pCs 0 )*(  (1) whereC ij issetto 1ifthe inputvectori(L i ,R i ) matchesthetwocorrespondingleftandrightCSS contextofthequerystructurej,and0otherwise.p j  is the possibility of occurrence of the j th  query structure,andnisthetotalnumberofthestructures intheQueryModel.NotethatEquation(1)givesa detectedCSShigherweightifitmatchestomore querystructureswithhigheroccurrenceprobabili- ties. We simply select the best CSS i  such that )(maxarg i i s accordingtoEqn(1). Forillustration,let’sconsidertheaboveexample with2detectedCSSs.ThetwoCSSvectorsare:[6, 9, 4] and [7, 9, 3]. From the Query Model, we know that the probability of occurrence, p j , of structure[6,9,4]is0,andthatofstructure[7,9,3] is0.03,withthelattermatchestoonlyonestruc- ture.Hencethes i valuesforthemare0and0.03 respectively.Thusthemostprobablecoresemantic structureis[7,9,3]andtheCSS“ (half) (pull) (light)”isextracted. 4 QueryTermsGeneration Becauseofspeechrecognitionerror,theCSSob- tained is likely to contain error, or in the worse case,missingthemainsemanticsofthequeryalto- gether.Wenowdiscusshowwealleviatetheerrors inCSSfortheformercase.Wewillfirstbreakthe CSS into one or more basic semantic parts, and thenapplythemulti-tiermethodtomapthequery componentstoknownphrases. 4.1BreakingCSSintoBasicComponents Inmanycases,theCSSobtainedmaybemadeup ofseveralsemanticcomponentsequivalenttobase nounphrases.Hereweemployatechniquebased onChinesecutmarks(Wang1992)toperformthe segmentation. The Chinese cut marks are tokens that can separate aChinesesentence into several semanticparts.Zhou(1997)usedsuchtechniqueto detectnewChinesewords,andreportedgoodre- sults with precision and recall of 92% and 70% respectively.ByseparatingtheCSSintobasickey components,wecanlimitthepropagationoferrors. 4.2Multi-tierquerytermmapping Inordertofurthereliminatethespeechrecognition errors,weproposeamulti-tierapproachtomapthe basic componentsin CSS into known phrases by usingacombinationofmatchingtechniques.Todo this,weneedtobuildupaphrasedictionarycon- taining typical conceptsused ingeneral and spe- cificdomains.MostbasicCSScomponentsshould bemappedtooneofthesephrases.Thusevenifa basiccomponentcontainserrors,aslongaswecan findasufficientlysimilarphraseinthephrasedic- tionary, wecanusethisinplaceoftheerroneous CSScomponent,thuseliminatingtheerrors. We collected a phrase dictionary containing about32,842phrases,covering mostlybasenoun phraseandnamedentity.Thephrasesarederived fromtwosources.We firstderivedasetofcom- mon phrases from the digital dictionary and the logsinthesearchengineusedattheShanghaiJiao TongUniversity.Wealsoderivedasetofdomain specific phrases by extracting the base noun phrasesandnamedentitiesfromtheon-linenews articlesobtainedduringtheperiod.Thisapproach isreasonableasinpracticewecanuserecentweb ornewsarticlesto extractconceptstoupdatethe phrasedictionary. Given the phrase dictionary, the next problem then is to map the basicCSS components tothe nearest phrases in the dictionary. As the basic componentsmaycontainerrors,wecannotmatch them exactly just at the character level. We thus propose to match each basic component with the knownphrasesinthedictionaryatthreelevels:(a) character level; (b) syllable string level; and (c) confusion syllable string level. The purpose of matching at levels b and c is to overcome the homophoneprobleminCSS.Forexample,“  (Laden)” is wrongly recognized as “  (pull lamp)”bythespeechrecognitionengine.Sucher- rorscannotbere-solvedatthecharactermatching level,butitcanprobablybematchedatthesyllable stringlevel.Theconfusionmatrixisusedtofurther reducetheeffectofspeechrecognitionerrorsdue tosimilarsoundingcharacters. To account for possible errors in CSS compo- nents, we perform similarity, instead of exact, matchingatthethreelevels.GiventhebasicCSS componentq i ,andaphrasec j inthedictionary,we compute: = = ),( 0 * |}||,max{| ),( ),( ii cqLCS k k ii ii ii M cq cqLCS cqSim (2) where LCS(q i ,c j )gives the number of characters/ syllablematchedbetweenq i andc i intheorderof theirappearanceusingthelongestcommonsubse- quence matching (LCS) algorithm (Cormen et al 1990).M k isintroducedtoaccountsforthesimilar- itybetweenthetwomatchingunits,andisdepend- ent on the level of matching. If the matching is performedatthecharacterorsyllablestringlevels, thebasicmatchingunitisonecharacteroronesyl- lableandthesimilaritybetweenthetwomatching unitsis1.Ifthematchingisdoneattheconfusion syllablestringlevel,M k isthecorrespondingcoef- ficientsintheconfusionmatrix.HenceLCS(q i ,c j ) givesthedegreeofmatchbetweenq i andc j ,nor- malizedbythemaximumlengthofq i orc j ;andΣM gives the degree of similarity between the units beingmatched. Thethreelevelofmatchingalsorangesfrombe- ingmoreexactatthecharacterlevel,tolessexact attheconfusionsyllablelevel.Thusifwecanfind a relevant phrase with sim(q i ,c j )>  at the higher characterlevel,wewillnotperformfurthermatch- ing at the lower levels. Otherwise, we will relax theconstrainttoperformthe matchingatsucces- sivelylowerlevels,probablyattheexpenseofpre- cision.  Thedetailofalgorithmislistedasfollows: Input:BasicCSSComponent,q i  a. Matchq i withphrasesindictionaryatcharacter levelusingEqn.(2). b. Ifwecannotfindamatch,thenmatchq i with phrasesatthesyllablelevelusingEqn.(2). c. Ifwestillcannotfindamatch,matchq i with phrasesattheconfusionsyllablelevelusing Eqn.(2). d. Ifwefoundamatch,setq’ i =c j ;otherwiseset q’ i =q i . Forexample,givenaquery:“ ”(pleasetellmesomenewsabout Iraq).Ifthequeryiswronglyrecognizedas“ ”. If, however, we couldcorrectly extracttheCSS“ (Iraq) fromthismis-recognizedquery,thenwecouldig- norethespeechrecognitionerrorsinotherpartsof the above query. Even if there are errors in the CSSextracted,suchas“ (chen) (waterside)” insteadof“ (chenshuibian)”,wecouldap- plythesyllablestringlevelmatchingtocorrectthe homophone errors. For CSS errors such as “  (corrupt) (usually)”insteadofthecorrectCSS “ (Taliban)”, which could not be corrected atthesyllablestringmatchinglevel,wecouldap- plytheconfusionsyllablestringmatchingtoover- comethiserror. 5 Experimentsandanalysis Asoursystem aimsto correct theerrorsand ex- tractCSScomponentsinspokenqueries,itisim- portant todemonstrate thatour system is able to handlequeriesofdifferentcharacteristics.Tothis end,wedevisedtwosetsoftestqueriesasfollows. a)Corpuswithshortqueries We devised 10 queries, each containing a CSS withonlyonebasiccomponent.Thisisthetypical typeofqueriesposedbytheusersontheweb.We asked10 differentpeopleto “speak” thequeries, and used the IBM ViaVoice 98 to perform the speechtotextconversion.Thisgivesrisetoacol- lectionof100spokenqueries.Thereisatotalof 1,340Chinesecharactersinthetestquerieswitha speechrecognitionerrorrateof32.5%. b)Corpuswithlongqueries  Inordertotestonqueriesusedinstandardtest corpuses,weadoptedthequerytopics(1-10)em- ployed in TREC-5Chinese-Languagetrack.Here each query contains more thanone key semantic component.Werephrasedthequeriesintonatural languagequeryformat,andaskedtwelvesubjects to “read” the queries. We again used the IBM ViaVoice98toperformthespeechrecognitionon theresulting120 differentspokenqueries,giving risetoatotalof2,354Chinesecharacters witha speechrecognitionerrorrateof23.75%. Wedevisedtwoexperimentstoevaluatetheper- formance of ourtechniques.The firstexperiment wasdesignedtotesttheeffectivenessofourquery model in extracting CSSs. The second was de- signedtotesttheaccuracyofouroverallsystemin extractingbasicquerycomponents.  5.1Test1:AccuracyofextractingCSSs The test results show that by using our query model,wecouldcorrectlyextract99%and96%of CSSs from the spoken queries for the short and long query category respectively. The errors are mainly due to the wrong tagging of some query components,whichcausedthequerymodeltomiss the correct querystructure, or match to a wrong structure. Forexample:giventhequery“ ”(pleasetellmesomenewsabout Taliban).Ifitiswronglyrecognizedas:     97910 which is a nonsensical sentence. Since the prob- abilitiesofoccurrencebothquerystructures[0,9,7] and[7,9,10]are0,wecouldnotfindtheCSSatall. Thiserrorismainlyduetothemis-recognitionof thelastquerycomponent“ (news)”to“  (afternoon)”.ItconfusestheQueryModel,which couldnotfindthecorrectCSS. Theoverallresultsindicatethattherearefewer errorsinshortqueriesassuchqueriescontainonly one CSS component. This is encouraging as in practicemostusersissueonlyshortqueries. 5.2Test2:Accuracyofextracting basic query components In order to test the accuracy of extracting basic querycomponents,weaskedonesubjecttomanu- ally divide the CSS into basic components, and used that as the ground truth. We compared the followingtwomethodsofextractingCSScompo- nents: a) As a baseline, we simply performed the stan- dardstopwordremovalanddividedthequery intocomponentswiththehelpofadictionary. However, there is no attempt to correct the speechrecognitionerrorsinthesecomponents. Hereweassumethatthenaturallanguagequery isabagofwordswithstopwordremoved(Ri- cardo,1999).Currently,mostsearchenginesare basedonthisapproach. b)WeappliedourquerymodeltoextractCSSand employed the multi-tier mapping approach to extractandcorrecttheerrorsinthebasicCSS components. Tables 3 and 4 give the comparisons between Methods(a)and(b),whichclearlyshowthatour methodoutperformsthe baselinemethodbyover 20.2% and20%inF 1 measure fortheshortand longqueriesrespectively. Table3:ComparisonofMethodsaandbforshortquery  Average Precision  Average Recall F 1  Methoda  31% 58.5% 40.5% Methodb  53.98% 69.4% 60.7%  +22.98%  +10.9% +20.2% Table4:ComparisonofMethodsaandbforlongquery  Average Precision  Average Recall F 1  Methoda  39.23% 85.99% 53.9% Methodb  67.75% 81.31% 73.9%  +28.52%  -4.68% +20.0% Theimprovementislargelyduetotheuseofour approach to extract CSS and correct the speech recognition errors in the CSS components. More detailedanalysisoflongqueriesinTable3reveals thatourmethodperformsworsethanthebaseline method in recall. This is mainly due to errors in extracting and breaking CSS into basic compo- nents. Although we used the multi-tier mapping approachtoreducetheerrorsfromspeechrecogni- tion, its improvement is insufficient to offset the lost in recallduetoerrors inextractingCSS.On theotherhand, fortheshortquerycases,without theerrorsinbreakingCSS,oursystemismoreef- fectivethanthebaselineinrecall.Itisnotedthatin bothcases,oursystemperformssignificantlybet- terthanthebaselineintermsofprecisionandF 1  measures. 6 Conclusion Althoughresearchonnaturallanguagequeryproc- essingandspeechrecognitionhasbeencarriedout formanyyears,thecombinationofthesetwoap- proachesto help a large population of infrequent usersto“surfthewebbyvoice”hasbeenrelatively recent. This paper outlines a divide-and-conquer approachtoalleviatetheeffectofspeechrecogni- tionerror,andinextractingkeyCSScomponents foruseinastandardsearchenginetoretrieverele- vantdocuments.Themaininnovativestepsinour system are: (a) we use a query model to isolate CSSinspeechqueries;(b)webreaktheCSSinto basiccomponents;and(c)weemployamulti-tier approach tomapthebasiccomponentstoknown phrases in the dictionary. The tests demonstrate thatourapproachiseffective. Theworkisonlythebeginning.Furtherresearch canbecarriedoutasfollows.First,asmostofthe queriesareaboutnamedentities suchastheper- sonsororganizations,weneedtoperformnamed entityanalysis onthequeriestobetterextractits structure,andinmappingtoknownnamedentities. Second,mostspeechrecognitionenginewillreturn a list of probable words for each syllable. This couldbeincorporatedintoourframeworktofacili- tatemulti-tiermapping. References BerlinChen,Hsin-minWang,andLin-ShanLee (2001),“ImprovedSpokenDocumentRetrieval byExploringExtraAcousticandLinguistic Cues”,Proceedingsofthe7thEuropeanConfer- enceonSpeechCommunicationandTechnology locatedat http://homepage.iis.sinica.edu.tw/ 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Extracting Key Semantic Terms from Chinese Speech Query for Web Searches  GangWANG  NationalUniversityof Singapore wanggang_sh@hotmail.com   Tat-SengCHUA  NationalUniversityofSinga- pore chuats@comp.nus.edu.sg  Yong-ChengWANG  ShanghaiJiaoTongUniver- sity,China,200030 ycwang@mail.sjtu.edu.cn Abstract This. re- searchisindevisingadivide-and-conquerstrategy toalleviatethe speech recognition errors. It uses the query modeltofacilitatetheextractionofmain core semantic string(CSS) from the Chinese natu- rallanguage speech query. ItthenbreakstheCSS into. analyzethe query and extract the core semantic string (CSS) that containsthemain semantic ofthe query. Thereare twomaincomponents for a query model.Thefirst is query componentdictionary,which