ÿWPCO ÀVÔdf¥~t£SÊuÞûH¡mY µÿÌÓƒ±Lð¡µF{[u‡àŒ¼¯·Ä 9¾Î·â% h¾ìÕ>†?ÃÀí›U©“ .¯Y"Õìò9{ׂ®Ñݘ…>…Ò–Ó˜«^¿ÄG´ñB{$§Û?ÒåP·­'h“0^M€ú@¸nFÐ,æSbxÔ7¿·Ü œÈ$#CDúOm_‡'|ŽïKÆÆj#ÅÍØPÚ…´„bsw UV°8±^›sXSøSý“;]é•“]Šט"»7väܧÈ,mÌñ©Ó.¿«Hy<%‘½Öæ-¿rÄst^†kÛKz®2}àë[qToY÷ÿ;…ñÞC¹)I$&U¾ZÕÆúF‰WÊæ1GYê§/&aÄ4Çøcc4¨zšòÊ.t‚ºÞÒ™˜öÏ-c뇲ÐRžð½z4tqnâ»Ûvœe8zžEoP‘<ã´XJa ŸhAaSÎiÜ ¤¶%!|à'˜šž©ÚüJÇe¬€’ö¥aÀLq_Õz‘;ºê~ä**^ö—L BÂÍЬWe¡hø5@©´&ù¬Œ–ïAŸv³kÆ4â™7Âþìö$•g%Z²iT£š–É Œ?¤æV ãSt²ÆA½Teã¹~,#†hUNî %< 1mB 0b¯ 1Ç 0!bØU<:4v 0(Š² RÁ 00y8Œ 0TÄ 0 B5 AMR ÆŸ 0De 0J© D/ó B" D3?Ær 0D8 0(| 0¤ 0§4 AMÛa( 0D‰ B*Í B÷÷òše  0DpI 0D¹¹ RýHP LaserJet IIIPHP3P.PRS,\,,\,ð*ØP _IzŸe6Gî#O(@øÐ Z ‹6Times New Roman RegularX#%e37=CIQYag­­1.a.i.(1)(a)(i)1)a)(:ê3$´´Ô2P¸ÔÚ  Ú0Ú  ÚÔ3  Ôà0  à CP¸37;CO_s‹§¯¯11.11.1.11.1.1.11.1.1.1.11.1.1.1.1.11.1.1.1.1.1.11.1.1.1.1.1.1.1(:ê3$¼¼Ô2P¸ÔÚ  Ú0Ú  ÚÔ3  Ôà0  àô\  `$Times NewRomanzª×"ÿÿ($¡¡3|xëPaper¼¼Ô2DDÔÚ  Ú0Ú  ÚÔ3  Ôà0  à(Q©|$½½Ô2P¸ÔÚ  Ú0Ú  Ú.Ú  Ú0Ú  ÚÔ3  Ôà0  àCreation of LKBs (w/refs)Í7 '  (—8$——ÔªÔò òòòóóó óÔªÔ<wjÿÿ:Default ParaÔ€XXXÔÔQ€ô\  `$Times NewRomanQÔÔ  ÔÔ€XXXÔÔQ€ô\  `$Times NewRomanQÔ  dAÿÿ<< Bÿÿ‚Level 1Level 2Level 3Level 4Level 5(3¯#$££Ý ƒ !ÝÝ  Ý(üP$““ò òTable€Ú  Ú1Ú  Úó ó'÷ÿ dxdüÿP Pd'ÿÿÈÈÈÈdxd Level 1 Level 2 Level 3 Level 4 Level 5(3¯#$¢¢Ý ƒ !ÝÝ  Ý(ÿÿ$’’(hâ‹$¾¾Ô2P¸ÔÚ  Ú0Ú  Ú.Ú  Ú0Ú  Ú.Ú  Ú0Ú  ÚÔ3  Ôà0  à(Ás$¿¿Ô2P¸ÔÚ  Ú0Ú  Ú.Ú  Ú0Ú  Ú.Ú  Ú0Ú  Ú.Ú  Ú0Ú  ÚÔ3  Ôà0  àA<< cÿÿ WÝ ƒ ¯#)ÝÝ  Ýà€  F(#àò òÔ€»3Ì»XXÔDRAFTó ó(3¯#$©©Ý ƒ !ÝÝ  Ý&ÿÿ d dûÿX Xd èÝ ƒ¯#"ÝÝ  ÝÓ$ƒpX°œX$ÓCauseà0  àDomainà0°°àHypernymà0°°àLocationа° ÐMannerà0  àMaterialà0°°àMeansà0°°àPartÐéé°° ÐPossessorà0  àPurposeà0°°àQuasi„Hypernymà0°°àSynonymÐÒÒ°° ÐTimeà0  àTypical€Objectà0°°àTypical€Subjectà0°°àUser Ý ƒ¯##ÝÝ  ÝÝ ƒüPÝò òTable€Ú  Ú1Ú  Úó óÝ  Ý:€€Relations€Automatically€Created€in€Microsoft€Analysis [Ý ƒ'¯#'ÝÝ  ÝòòCL€Researchóóà€4(#àòòLexical€Knowledge€Basesóó(3¯#$§§Ý ƒ !ÝÝ  Ý fÝ ƒ)¯#*ÝÝ  ÝJuly€1997à@ººˆìàÚ  Ú1Ú  Úˆà€‚!‚!‚!(#àDraft(3¯#$ªªÝ ƒ !ÝÝ  ÝH0§ÿв 7\dqÀ Ø'ðÝ ƒ !ÝÝ  ÝÓC€   P¸C#CÓñëñÐ  ° ÐñëñÑ  ÑÖ€ÿÿÖà@º º ìàò òAutomatic€Creation€of€Lexical€Knowledge€Bases:ó óˆÐ ° Ðà@Î Î ìàò òNew€Developments€in€Computational€Lexicologyó óˆÐ šê ÐÌà@ìàò òTechnical€Report€97„03ó óˆÐ m½ ÐÌÌà@TT&ìàò òJuly€1997ó óˆÐ ) y ÐÌà@>> ìàò òKenneth€C.€Litkowskió óˆÐ ü L Ðà@¾¾%ìàò òCL€ResearchˆÐ æ 6  Ðà@DD ìà20239€Lea€Pond€PlaceˆÌà@¹¹ìàGaithersburg,€Maryland€20879ó óˆÐ  º  ÐØØÑ  ÑÖ€ÖÖ€&ÿÿÖÖ€(ÿÿÖà@º º ìàò òòòAutomatic€Creation€of€Lexical€Knowledge€Bases:óóó óˆÐ a Ðà@Î Î ìàò òòòNew€Developments€in€Computational€Lexicologyóóó óˆÐ Kê ÐÌà@xx&ìàò òAbstractó óˆÐ  ½ ÐÌÓ  Óà8  àText€processing€technologies€require€increasing€amounts€of€information€about€wordsÏand€phrases€to€cope€with€the€massive€amounts€of€textual€material€available€today.€ÏInformation€retrieval€search€engines€provide€greater€and€greater€coverage,€but€do€notÏprovide€a€capability€for€identifying€the€specific€content€that€is€sought.€€GreaterÏreliance€is€placed€on€natural€language€processing€(NLP)€technologies,€which,€in€turn,Ïare€placing€an€increasing€reliance€on€semantic€information€in€addition€to€syntacticÏinformation€about€lexical€items.€€The€structure€and€content€of€lexical€entries€has€beenÏincreasing€rapidly€to€meet€these€needs,€but€obtaining€the€necessary€information€forÏthese€lexical€knowledge€bases€(LKBs)€is€a€major€problem.€€ComputationalÏlexicology,€which€began€in€somewhat€halting€attempts€to€extract€lexical€informationÏfrom€machine„readable€dictionaries€(MRDs)€for€use€in€NLP,€is€seeing€the€emergenceÏof€new€techniques€that€offer€considerable€promise€for€populating€and€organizingÏLKBs.€€Many€of€these€techniques€involve€computations€within€the€LKBs€themselvesÏto€create,€propagate,€and€organize€the€lexical€information.Ð Ð Ð  ÐÓÓÌò òÝ ‚ê3 ÝÝ  ÝÝ‚ê3ÂÝÔ2P¸ÔÚ  Ú1Ú  ÚÔ3  Ôà0  àÝ  ÝIntroduction݃ê3Âí݌И7(#(# ÐŒÝ  ÝÌà  àó óComputational€lexicology€began€in€the€late€1960s€and€1970s€with€attempts€to€extractÐ l  Ðlexical€information€from€machine„readable€dictionaries€(MRDs)€for€use€in€natural€languageÏprocessing€(NLP),€primarily€in€extracting€hierarchies€of€verbs€and€nouns.€€During€the€1980s,ÏNLP€began€reaching€beyond€syntactic€information€with€a€greater€reliance€on€semanticÏinformation,€locating€this€information€within€the€lexicon.€€After€reaching€a€conclusion€(in€theÏearly€1990s)€that€insufficient€information€could€be€obtained€about€lexical€items€from€MRDs,€newÏtechniques€have€emerged€to€offer€considerable€promise€for€populating€and€organizing€lexicalÏknowledge€bases€(LKBs).€€An€underlying€reason€for€the€realization€of€these€techniques€seems€toÏbe€the€increasing€capability€to€deal€with€the€large€amount€of€data€that€must€be€digested€to€dealÏwith€the€overall€content€and€complexity€of€semantics.ÌÌà  àThis€discussion€begins€with€the€assumptions€about€large€amounts€of€information€inÏlexical€entries€and€particular€computations€made€with€this€information€in€NLP.€€From€thisÏstarting€point,€the€paper€describes€emerging€techniques€for€populating€and€propagatingÏinformation€to€lexical€entries€derived€from€existing€information€with€the€LKB.€€The€primaryÏmotivations€for€extending€lexical€entries€comes€from€a€need€to€provide€greater€internalÏconsistency€in€the€LKB€and€from€an€apparently€insatiable€requirement€for€greater€amounts€ofÏinformation€to€support€demands€from€very€unlikely€sources.Ìâ âÐ Î*m$( Ðà  àThe€first€set€of€techniques€that€are€described€revolve€around€more€detailed€analysis€ofÏâ âdefinitions€from€MRDs,€focusing€on€research€from€Microsoft,€with€elaborations€in€attempts€toÏarticulate€conceptual€clusters.€€Next,€several€avenues€of€research€have€developed€techniques€forÏcreating€new€categories€out€of€existing€hierarchies,€dynamically€cutting€across€hierarchical€links,Ïfrequently€in€response€to€domain„specific€processing€of€text.€€The€status€of€lexical€rules,€whichÏprovide€characterizations€of€how€new€entries€and€senses€are€derived€from€existing€entries€andÏsenses,€has€been€refined€in€ways€that€are€closer€to€the€way€language€uses€these€rules€and€thatÏpermit€the€variation€in€phrase€structure.€€The€last€section€discusses€the€potential€of€an€overallÏtheory€of€the€lexicon€arising€from€a€formalization€of€semantic€networks€with€the€theory€ofÏlabeled€directed€graphs.ÌÌò òÝ ‚ê3 ÝÝ  ÝÝ‚ê3,ÝÔ2P¸ÔÚ  Ú2Ú  ÚÔ3  Ôà0  àÝ  ÝÔ% € ÔAssumptions€about€contents€of€lexical€entriesó ó݃ê3,WÝŒÐd (#(# ÐŒÝ  ÝÌà  àA€lexicon€begins€with€a€simple€listing€of€word€forms,€and€may€be€initially€extended€toÏinclude€phrasal€entries.€€We€would€expect€a€next€extension€to€include€information€found€in€anÏordinary€paper€dictionary:€€inflectional€forms,€parts€of€speech,€definitions,€and€perhaps€usageÏinformation,€pronunciation,€and€etymology.€€Lexicons€used€in€some€form€of€computerized€textÏprocessing€(such€as€information€retrieval,€natural€language€processing,€machine€translation,€andÏcontent€analysis)€are€requiring€ever„increasing€amounts€of€structure€and€content€associated€withÏeach€entry.ÌÌà  àInformation€retrieval€lexicons€(thesauruses)€create€links€between€items,€indicating€thatÏone€entry€is€broader€than,€narrower€than,€or€equivalent€to€another€entry.€€Natural€languageÏprocessing€requires€syntactic€information€about€each€entry,€primarily€in€the€specification€ofÏsubcategorization€patterns€(that€is,€syntactic€structures€likely€to€appear€in€the€surroundingÏcontext).€€Machine€translation€makes€use€of€simple€correspondences,€much€like€thesauruses,Ïmerely€equating€words€in€the€source€language€to€words€in€the€target€language€(the€transferÏmodel),€but€this€model€doesn't€always€hold€true€because€concepts€are€expressed€differently€inÏdifferent€languages,€thus€requiring€more€information€about€the€conceptual€and€structural€contentÏof€lexical€entries€(the€interlingua€model).€€Content€analysis€requires€lexicons€that€are€brokenÏdown€into€categories,€themes,€or€subject€areas.ÌÌà  àAs€a€result€of€developments€in€the€fields€noted€above,€lexical€entries€today€may€includeÏcategorical€information€(part€of€speech),€inflectional€and€perhaps€morphologically€derived€forms,Ïsyntactic€and€semantic€features€(typically€boolean€information),€information€about€syntacticÏstructure,€semantic€information€that€places€the€lexical€item€with€a€world€view€(an€ontology),€andÏmiscellaneous€information€that€characterizes€a€word's€pragmatic€usage.€€(Nirenburg,€et€al.€1992)Ïprovide€the€most€complete€range€of€information€in€a€lexical€entry,€including€category,Ïorthography,€phonology,€morphology€(irregular€forms,€paradigm,€and€stem„variants),Ïannotations,€applicability€(such€as€field€and€language),€syntactic€features€(binary€values€such€asÏòòcountóó,€multiple€values€such€as€òònumberóó),€syntactic€structure€(subcategorization€patterns),Ð Ê*i$( Ðsemantics€(semantic€class€and€lexical€mapping€from€syntactic€patterns€to€role€values),€lexicalÐ ³+R%) Ðrelations,€lexical€rules,€and€pragmatics€(including€stylistic€information€and€analysis€triggers€toÏcharacterize€domain€and€text€relations).€€As€described€in€(Nirenburg,€et€al.€1995),€entries€fromÏother€systems€may€be€mappable€into€an€ontologically„based€lexical€entry.ÌÌà  àThere€are€four€aspects€of€the€Text€Meaning€Representation€and€Ontology€of€Nirenburg'sÏMikroKosmos€system€where€extension€of€the€information€may€be€possible:€€(1)€semanticÏrelations€with€other€entries,€perhaps€not€highlighted€as€well€as€in€other€systems€that€are€overtlyÏcharacterized€as€semantic€networks,€such€as€the€Unified€Medical€Language€System€at€theÏNational€Library€of€Medicine€(this€semantic€network€includes€a€highly€elaborated€set€of€56Ïsemantic€relations,€themselves€presented€in€a€hierarchy);€(2)€identification€of€collocationalÏpatterns€associated€with€a€lexical€entry€(such€as€Mel'ðcðuk's€functional€specifications);€€(3)€internalÐ {  Ðstructure€of€the€different€senses€of€a€lexeme,€particularly€showing€any€derivational€relationshipsÏbetween€senses€and€allowing€for€underspecification€(that€is,€supersenses€that€are€ambiguous€withÏrespect€to€particular€features€present€in€subsenses);€and€(4)€identification€of€the€logicalÏconstraints,€preconditions,€effects,€and€decomposition€of€meaning€associated€with€use€of€theÏlexical€item.ÌÌà  àBased€on€the€foregoing,€a€general€assumption€is€that€all€possible€information€about€eachÏlexical€item€is€to€be€obtained€and€placed€in€the€lexicon.€€If€there€are€additional€types€ofÏinformation€beyond€that€identified€thus€far,€the€assumption€is€that€it€will€be€useful€to€includeÏsuch€information€in€the€lexicon.€€Typically,€the€specific€information€included€in€the€lexicon€isÏdriven€by€the€application€and€may€be€optimized€in€some€way€to€facilitate€use€within€thatÏapplication.€€This€means€that€only€pertinent€information€for€an€application€is€extracted€from€theÏlexical€knowledge€base.€€(Of€course,€many€applications€may€never€need€to€develop€all€theÏinformation€that€may€be€associated€with€a€lexical€item.)ÌÌò òÝ ‚ê3 ÝÝ  ÝÝ‚ê3õ%ÝÔ2P¸ÔÚ  Ú3Ú  ÚÔ3  Ôà0  àÝ  ÝAssumptions€about€current€computations€in€the€lexiconó ó݃ê3õ% &݌Рª(#(# ÐŒÝ  ÝÌà  àHistorically,€information€in€a€lexicon€has€simply€been€accessed€for€subsequent€processingÏin€an€application€area.€€In€the€mid€1980s,€an€observation€was€made€in€the€development€ofÏGeneralized€and€Head„Driven€Phrase€Structure€Grammars€(GPSG,€HPSG)€that€the€lexicon€couldÏbe€the€repository€of€information€that€could€replace€and€facilitate€many€of€the€control€structuresÏused€in€natural€language€processing.€€Since€that€time,€many€systems€have€been€developed€thatÏhave€placed€increasing€reliance€on€the€lexicon.€€This€led€to€the€development€of€binding€andÏunification€techniques€that€make€it€possible€for€information€from€separate€lexical€entries€toÏcombine€with€one€another.€€In€addition,€these€techniques€made€it€possible€to€structure€the€lexiconÏinto€an€inheritance€hierarchy,€so€that€it€is€not€necessary€to€put€redundant€information€in€everyÏlexical€entry.€€(The€precise€form€of€inheritance€is€an€area€of€considerable€research€today,€withÏ(Davis€1996)€providing€a€semantic€hierarchy.)ÌÌà  àIn€a€separate€vein,€a€considerable€industry€had€evolved€for€analyzing€machine„readableÏdictionaries€(MRDs).€€It€had€been€found€that€ordinary€dictionaries€contained€much€informationÐ ³+R%) Ðthat€could€be€used€in€a€variety€of€natural€language€processing€tasks,€and€so,€attempts€were€madeÏto€convert€such€information€into€appropriate€forms.€€Along€with€these€attempts,€it€was€foundÏpossible€to€extract€hierarchies€from€these€MRDs€(although€fraught€with€a€major€difficulty€inÏidentifying€the€particular€sense€in€which€words€were€used€to€ensure€the€validity€of€the€hierarchy).ÌÌò òÝ ‚ê3 ÝÝ  ÝÝ‚ê32-ÝÔ2P¸ÔÚ  Ú4Ú  ÚÔ3  Ôà0  àÝ  ÝComputations€for€populating€and€propagating€lexical€entriesó ó݃ê32-]-ÝŒÐî (#(# ÐŒÝ  ÝÌà  àThe€development€of€an€LKB€is€generally€considered€to€be€an€extremely€labor„intensiveÏeffort,€with€each€entry€hand„crafted.€€Analysis€of€MRDs€has€attempted€to€automate€some€of€thisÏeffort,€but€it€is€difficult€to€see€where€results€from€such€efforts€have€actually€been€used.€€It€seemsÏas€if€no€progress€is€being€made,€so€that€each€new€report€in€the€literature€may€provide€newÏobservations,€but€there€is€little€sense€of€an€accumulation€of€knowledge,€of€the€establishment€ofÏan€LKB€that€is€amenable€to€evolution€and€expansion.€€Moreover,€(Richardson€1997:€132)€statedÏthat€the€import€of€(Ide€&€Veronis€1993)€and€(Yarowsky€1992)€was€to€suggest€that€"LKBs€createdÏfrom€MRDs€provide€spotty€coverage€of€a€language€at€best."€€Except€for€the€efforts€at€Microsoft,Ïit€appears€that€there€are€presently€no€major€projects€aimed€at€extracting€LKB€material€fromÏMRDs.€€To€some€extent,€dictionary€publishers€are€making€more€direct€electronic€use€of€theirÏmaterials,€but€this€work€generally€is€merely€an€electronic€version€of€the€paper€dictionaries,€withÏlittle€view€of€an€entirely€different€structure€optimized€for€text€processing€applications.ÌÌà  àPerhaps€these€difficulties€require€a€different€perspective€on€the€nature€of€a€lexicon.€ÏPersonal€and€general€(i.e.,€dictionary)€lexicons€undergo€continuing€evolution€and€extension.€ÏThis€suggests€that€computational€lexicons€need€to€be€engineered€with€this€in€mind.€€LKBs€areÏdynamic€entities€that€will€undergo€almost€continual€revision.€€An€LKB€is€an€entity€that€sits€apartÏfrom€any€use€we€make€of€it,€and€while€it€is€sitting€there€off„line,€and€should€be€undergoing€aÏcontinual€process€of€expansion€and€reorganization.€€At€any€time,€subsets€of€the€information€fromÏthe€LKB€are€extracted€for€use€in€a€particular€application.ÌÌà  àThis€process€of€expansion€and€reorganization€can€be€very€dynamic;€lexicon€updateÏshould€be€able€to€occur€within€the€span€of€analysis€of€a€single€document.€€A€single€document€canÏcontain€its€own€sublanguage,€and€may€introduce€new€ontological€facts€and€relations€and€may€useÏexisting€lexical€items€in€novel€ways€that€are€not€present€in€the€current€LKB.€€There€areÏreasonably€well„known€lexical€processes€by€which€these€new€ontological€and€sense€data€areÏadded€manually.€€We€may€now€be€at€a€sufficient€state€of€progress€that€these€processes€can€beÏautomated€to€provide€the€kind€of€dynamic€LKB€that€we€need.ÌÌò òÝ ‚ê3 ÝÝ  ÝÝ‚ê3•7ÝÔ2P¸ÔÚ  Ú5Ú  ÚÔ3  Ôà0  àÝ  ÝMotivationsó ó݃ê3•7À7ÝŒÐ&'Å $(#(# ÐŒÝ  ÝÌà  àThe€greatest€problem€of€computational€linguistics€seems€to€be€the€acquisition€bottleneck,Ïspecifically€the€acquisition€of€new€lexical€items€(mostly€new€senses€of€existing€words,€that€is,Ïuses€of€existing€words€in€ways€that€are€only€slightly€different€from€what€may€be€present€in€theÏLKB)€and€new€pieces€of€knowledge€unknown€to€our€knowledge€base.€€(These€are€items€added€toÐ ´+S%) Ðsemantic€memory€and€to€episodic€memory.)€€To€deal€with€this€problem,€it€seems€necessary€toÏdesign€bootstrapping€techniques€into€the€knowledge€bases.€€These€bootstrapping€techniquesÏrequire€an€almost€continual€re„evaluation€of€the€data€in€our€lexicons€and€knowledge€bases,€toÏmake€new€computations€on€this€data€in€order€to€reassess€and€reconsider€each€component€part.ÌÌò òÝ ‚ ©| ÝÝ  ÝÝ‚ ©|s;ÝÔ2P¸ÔÚ  Ú5Ú  Ú.Ú  Ú1Ú  ÚÔ3  Ôà0  àÝ  ÝGreater€amount€of€information€availableó ó݃ ©|s;ž;ÝŒÐî (#(# ÐŒÝ  ÝÌà  àDevelopments€in€NLP€have€required€increasing€amounts€of€information€in€the€lexicon.€ÏIn€addition,€there€is€an€increasing€requirement€that€this€information€be€amenable€to€dynamicÏprocessing.€€Research€with€LKBs€that€have€a€static€structure€and€content,€such€as€WordNet,Ïincreasing€move€toward€expansion€of€information€and€cross„cutting€use€of€the€existing€structureÏand€organization.€€Different€types€of€applications€make€use€of€this€information€in€unanticipatedÏways.€€Data€dictionaries€for€database€applications,€articulation€of€primitives€for€such€things€asÏthe€Knowledge€Query€Manipulation€Language€and€Knowledge€Interchange€Format,€andÏterminological€databases€may€each€require€a€different€cut€on€an€LKB.ÌÌà  àThe€development€of€an€LKB€should€be€able€to€encompass€all€of€the€applications€that€mayÏeventually€rely€on€it.€€A€particular€application€would€be€able€to€extract€only€the€necessaryÏinformation€and€may€take€advantage€of€particular€storage,€representation,€and€accessÏmechanisms€for€efficiency€optimization.€€Every€opportunity€for€processing€text€can€beÏconsidered€as€an€opportunity€for€expanding€the€LKB.€€Every€opportunity€should€be€taken€toÏincrease€the€amounts€and€types€of€information€included€in€the€LKB.ÌÌò òÝ ‚ ©| ÝÝ  ÝÝ‚ ©|YAÝÔ2P¸ÔÚ  Ú5Ú  Ú.Ú  Ú2Ú  ÚÔ3  Ôà0  àÝ  ÝConsistency€of€lexiconó ó݃ ©|YA„AÝŒÐQð(#(# ÐŒÝ  ÝÌà  àGuidelines€are€generally€prepared€for€developing€lexicons€and€LKBs.€€As€much€asÏpossible,€these€guidelines€should€be€automated.€€More€specifically,€an€LKB€should€exhibit€aÏconsiderable€amount€of€internal€consistency.€€At€least€three€types€of€consistency€can€beÏenvisioned:€€(1)€circularity€should€be€rooted€out;€(2)€consistency€and€correctness€of€inheritanceÏshould€be€tested;€and€(3)€compositional€characteristics€of€lexical€items€should€be€checked.€ÏCompositional€characteristics€can€be€further€checked€externally€by€examination€of€actual€data.ÌÌò òÝ ‚ê3 ÝÝ  ÝÝ‚ê3ƒDÝÔ2P¸ÔÚ  Ú6Ú  ÚÔ3  Ôà0  àÝ  ÝDefinition€analysis€(forward€and€backward)ó ó݃ê3ƒD®D݌Ѓ#" (#(# ÐŒÝ  ÝÌà  à€(Amsler€1980)€provided€the€first€rigorous€attempt€to€analyze€dictionary€definitions,Ïbuilding€a€taxonomic€hierarchy€based€on€the€genus€words€of€a€definition.€€This€work€wasÏcontinued€at€IBM€in€the€early€1980s,€described€in€(Chodorow,€et€al.€1985),€further€attemptingÏautomatic€extraction€of€these€taxonomies.€€This€was€done€through€heuristic€pattern„matchingÏtechniques€to€identify€the€genus€terms€in€definitions€and€then€to€structure€them€in€a€hierarchy.ÌÌà  àSeveral€other€research€efforts€during€the€later€1980s€continued€analysis€of€dictionaryÏdefinitions€to€extract€information.€€(Markowitz,€et€al.€1986)€investigated€"semantically"Ð µ+T%) Ðsignificant€patterns€based€on€parsing€definitions€(with€the€linguistic€string€parser);€these€includedÏtaxonomy„inducing€patterns,€member„set€relations,€generic€agents€(in€noun€definitions),€suffixÏdefinitions,€identifying€action€verbs€from€noun€definitions€("the€act€of€Ving"),€selectionalÏinformation€for€verb€definitions,€and€recognizing€action€vs.€stative€adjectives.€€Other€workÏfocused€on€extracting€taxonomies€(Klavans,€et€al.€1990;€Copestake€1990;€Vossen€1991;€Bruce€&ÏGuthrie€1992).ÌÌà  à€(Richardson€1997)€says€that€this€work€overlooks€"the€true€tangled,€circular€nature€of€theÏtaxonomies€actually€defined€by€many€of€the€dictionary€genus€terms."€€Further,€he€cites€(Ide€&ÏVeronis€1993)€as€observing€that€"attempts€to€create€formal€taxonomies€automatically€fromÏMRDs€had€failed€to€some€extent,"€citing€"problems€with€circularity€and€inconsistency€...€in€theÏresulting€hierarchies."ÌÌò òÝ ‚ ©| ÝÝ  ÝÝ‚ ©|¤KÝÔ2P¸ÔÚ  Ú6Ú  Ú.Ú  Ú1Ú  ÚÔ3  Ôà0  àÝ  ÝMicrosoft€techniquesó ó݃ ©|¤KÏKÝŒÐ6Õ (#(# ÐŒÝ  ÝÌà  à€(Richardson€1997)€extracts€and€creates€16€bi„directional€relations€for€its€LKB€(calledÏMindNet).€€Microsoft€has€analyzed€147,000€definitions€and€example€sentences€from€(LongmanÏDictionary€of€Contemporary€English€1978)€(LDOCE)€and€the€(The€American€HeritageÏDictionary€of€the€English€Language€1992)€to€create€1.4€million€semantic€links€between€lexicalÏentries.€€The€basis€for€the€specific€links€is€the€use€of€structural€patterns€rather€than€just€stringÏmatching,€as€performed€in€earlier€work€(Montemagni€&€Vanderwende€1993).€€Table€1€shows€theÏrelations€automatically€created€by€parsing€in€creating€Microsoft's€MindNet.€€There€are€two€keyÏsteps€in€what€Microsoft€has€done:€(1)€parsed€the€definitions€and€example€sentences€with€a€broad„¼ñëñ¼ñëñcoverage€parser€and€(2)€included,€in€characterizing€a€word's€meaning,€all€instances€in€which€thatÏword€has€been€used€in€defining€other€words,€not€only€where€that€word€is€the€genus€term.€€AnÏexample€of€the€significance€of€the€latter€is€for€creating€meronymic€("part„of")€links€betweenÏentries.€€As€(Richardson€1997)€indicates,€the€parts€of€an€object€(say€"car")€are€seldom€describedÏin€the€dictionary€entry€for€that€object.€€However,€other€entries€(for€example,€"fender")€make€useÏof€the€object€in€their€definitions€(a€òòfenderóó€is€a€"guard€over€the€wheel€of€a€ò òcaró ó").€€RichardsonÐ Þ} Ðdistinguishes€between€semantic€relations€derived€by€analyzing€a€word's€definitions€(forward„¼ñëñ¼ñëñlinking)€and€those€derived€from€definitions€of€other€words€(backward„linking).€€Backward„¼ñëñ¼ñëñlinking€relations€are€known€as€"inverted€semantic€relation€structures"€and€are€stored€with€a€mainÏentry;€they€are€used€for€disambiguation€in€parsing€and€measurement€of€similarity.€€(When€aÏdefinition€is€parsed,€the€relations€structure€is€stored€at€that€entry.€€An€"inverted"€structure€isÏstored€at€all€other€words€identified€as€related.)Ìâ âÐ @&ß# Ðñëñ¼ñëñâ âñëñßO€$%>.*p| (`Ð `€À"OßñëññëñßO€$%>.*p| (`Ð `€À"Oßâ °±°±°(#°(#â°â °±°±°±°±â°â °±°±°±°±â°â °±°±°±°±â°â °±°±°±°±â°â °±°±°±°±â°â °±°±°±°±â°â °±°±°±°±â°â °(#°(#°±°±âñëñÌà  à€(Richardson€1997)€notes€that€much€of€the€work€attempting€to€create€networks€fromÏdictionary€definitions€in€building€LKBs€has€focused€on€quantitative€information€(that€is,Ïmeasuring€distance€between€nodes€in€the€network€or€measuring€semantic€relatedness€based€onÏco„occurrence€statistics).€€Instead,€he€focuses€on€labeling€semantic€relations€over€simple€co„¼ñëñ¼¼¼¼¼ñëñoccurrence€relations€and€distinguishing€between€paradigmatic€relatedness€(substitutionalÏsimilarity)€and€syntagmatic€relatedness€(occurring€in€similar€contexts).ÌÌà  àThis€important€component€of€the€Microsoft€use€of€MindNet€is€a€procedure€forÏdetermining€similarity€between€words€based€on€semantic€relations€between€them.€€A€semanticÏrelation€path€between€word1€and€word2€exists€when€word2€appears€in€word1's€forward„linkedÏstructure€or€in€any€of€word1's€inverted€relation€structures.€€Richardson€distinguishes€betweenÏparadigmatic€similarity€(òòmagazineóó€may€be€substituted€for€òòbookóó€in€many€contexts)€andÐ •4 Ðsyntagmatic€similarity€(òòwalkóó€and€òòparkóó€frequently€occur€in€the€same€context,€e.g.,€"a€walk€in€theÐ ~ Ðpart,"€but€cannot€be€substituted€for€one€another).€€Richardson€builds€similarity€measures€afterÏstudying€the€predominant€semantic€relation€paths€between€entries€(that€is,€path€patterns).ÌÌò òÝ ‚ ©| ÝÝ  ÝÝ‚ ©|5[ÝÔ2P¸ÔÚ  Ú6Ú  Ú.Ú  Ú2Ú  ÚÔ3  Ôà0  àÝ  ÝConceptual€clustersó ó݃ ©|5[`[ÝŒÐ"Á(#(# ÐŒÝ  ÝÌà  à€(Schank€&€Abelson€1977)€describes€an€elaborate€structure€of€scripts€(e.g.,€a€scenario€ofÏeating€in€a€restaurant),€intended€to€capture€events€made€up€of€more€than€one€element€andÏidentifying€objects€that€play€roles€in€the€events.€€(McRoy€1992)€says€that€a€text€will€generallyÏexhibit€lexical€cohesion€and€describes€conceptual€clusters,€defined€as€"a€set€of€senses€associatedÏwith€some€central€concept."€€She€distinguishes€three€types€of€clusters:€òòcategorialóó€(senses€sharingÐ ™"8 Ða€conceptual€parent),€òòfunctionalóó€(senses€sharing€a€specified€functional€relationship€such€as€part„Ð ‚#!  ÐñëñÐ ‚#!  ÐÐ ‚#!  ÐÐ ‚#!  ÐÐ ‚#!  ÐÐ ‚#!  Ðñëñwhole),€and€òòsituationalóó€(encoding€"general€relationships€among€senses€on€the€basis€of€their€beingÐ k$ ! Ðassociated€with€a€common€setting,€event,€or€purpose").€€Thus,€the€situational€cluster€forÏòòcourtroomóó€includes€senses€for€words€such€as€òòprisonóó,€òòcrimeóó,€òòdefendantóó,€òòtestifyóó,€òòperjureóó,€òòtestimonyóó,Ð =&Ü# Ðand€òòdefendóó.€€(Carlson€&€Nirenburg€1990),€in€describing€lexical€entries€that€can€be€used€in€worldÐ &'Å $ Ðmodeling,€envision€most€of€the€components€associated€with€scripts€and€conceptual€clusters,Ïparticularly€identifying€semantic€roles€(with€selectional€restrictions)€and€decomposition€of€eventÏverbs.€€(Richardson€1997)€describes€the€process€by€which€conceptual€clusters€can€be€identifiedÏfrom€MindNet€based€on€identifying€the€top€20€paths€between€query€words.€€He€notes€that€suchÏclusters€are€useful€not€only€in€word€sense€disambiguation€but€also€in€the€expansion€of€queries€inÐ ³+R%) Ðinformation€retrieval.€€The€specificity€of€the€relations€is€an€addition€to€previous€work.Ìâ âÌò òÝ ‚ ©| ÝÝ  ÝÝ‚ ©|±bÝÔ2P¸ÔÚ  Ú6Ú  Ú.Ú  Ú3Ú  ÚÔ3  Ôà0  àÝ  ÝFillmore's€Framesó ó݃ ©|±bÜb݌Р»(#(# ÐŒÝ  ÝÌà  à€(Lowe,€et€al.€1997)€outline€the€conceptual€underpinnings€of€an€effort€to€create€a€databaseÏcalled€FrameNet.€€Their€primary€purpose€is€to€produce€frame„semantic€descriptions€of€lexicalÏitems.€€They€note€the€lack€of€agreement€on€semantic€(case)€roles€and€observe€each€field€seems€toÏbring€a€new€set€of€more€specific€roles.€€They€suggest€that€many€lexical€items€evoke€genericÏevents€with€more€specific€characterizations€of€the€roles€and€that€they€instantiate€particularÏelements€of€the€frames.€€They€state€that€"any€description€of€word€meanings€must€begin€byÏidentifying€underlying€conceptual€structures"€which€can€be€encoded€in€frames€characterizingÏstereotyped€scenarios.€€They€recognize€the€importance€of€inheritance€in€encoding€lexical€items€inÏthis€way.ÌÌà  àThey€note€that€a€frame€(for€generic€medical€events,€for€example)€might€involve€detailedÏframe€elements€for€òòhealeróó,€òòpatientóó,€òòdiseaseóó,€òòwoundóó,€òòbodypartóó,€òòsymptomóó,€òòtreatmentóó,€and€òòmedicineóó.€Ð ò‘ ÐA€key€new€element€is€the€examination€from€corpus€analysis€of€the€frame€elements€from€a€givenÏframe€that€occur€in€a€phrase€or€sentence€headed€by€a€given€word€(calling€these€set€òòframe€elementÐ Äc Ðgroupsóó).€€They€would€identify€which€elements€of€a€frame€element€group€are€optional€or€impliedÐ ­L Ðbut€unmentioned.€€They€would€recognize€that€some€lexical€items€may€encode€multiple€frameÏelements€(for€example,€òòdiabeticóó€identifies€both€the€disorder€and€the€patient).€€In€summary,€theyÐ  Ðenvision€that€lexical€entries€will€include€full€semantic/syntactic€valence€descriptions,€with€frameÏelements€(for€at€least€verbs)€linked€to€a€specification€of€sortal€features,€indicating€the€selectionalÏand€syntactic€properties€of€the€constituents€that€can€instantiate€them.ÌÌà  à€(UMLS€knowledge€sources€1996),€with€its€elaborate€semantic€network€and€semanticÏrelation€hierarchy,€identifies€semantic€types€linked€by€the€various€relations,€and€thus€wouldÏclearly€satisfy€some€of€the€requirements€for€identifying€frame€elements€in€the€medical€field.ÌÌò òÝ ‚ ©| ÝÝ  ÝÝ‚ ©|ýkÝÔ2P¸ÔÚ  Ú6Ú  Ú.Ú  Ú4Ú  ÚÔ3  Ôà0  àÝ  ÝBarriere€techniquesó ó݃ ©|ýk(l݌а!O(#(# ÐŒÝ  ÝÌà  àRichardson€(personal€communication)€has€stated€that€Microsoft's€MindNet,€with€itsÏforward„linked€and€backward„linked€relational€structures,€essentially€identifies€conceptualÏclusters€associated€with€lexical€items.€€Indeed,€viewing€a€graphical€representation€of€someÏelements€of€MindNet,€with€lexical€entries€as€nodes€and€the€various€relations€as€labels€on€directedÏarcs€between€nodes,€it€is€clear€that€the€concepts€clustered€about€a€lexical€item€capture€the€ways€inÏwhich€that€lexical€item€may€be€used€in€ordinary€text.ÌÌà  à€(Barrið/ðre€&€Popowich€1996b)€have€also€extracted€semantic€structures€from€dictionaryÏdefinitions,€with€the€specific€objective€of€identifying€conceptual€clusters.€€They€note€that€muchÏearlier€work€with€MRDs€has€a€localist€orientation,€with€primary€concern€on€providingÐ ´+S%) Ðinformation€for€the€main€entries,€without€concern€for€the€relations€between€entries.€€They€provideÏa€bootstrapping€technique€to€create€Concept€Clustering€Knowledge€Graphs,€based€on€using€theÏconceptual€graphs€of€(Sowa€1984).€€They€start€with€a€trigger€word€and€expand€a€forward€searchÏthrough€its€definitions€and€example€sentences€to€incorporate€related€words.€€They€note€that€theÏclusters€formed€through€this€process€are€similar€to€the€(Schank€&€Abelson€1977)€scripts;Ïhowever,€they€make€no€assumptions€about€primitives.ÌÌà  àThey€start€by€forming€a€temporary€graph€using€information€from€closed€class€words€(òòwithóóÐ À _ Ðis€subsumed€by€òòinstrumentóó€in€a€relation€hierarchy),€relations€extracted€using€defining€formulas,Ð © H Ðand€relations€extracted€from€the€syntactic€analysis€of€the€definition€or€sample€sentence.€€TheyÏmake€use€of€a€concept€hierarchy€and€rules€that€provide€predictable€meaning€shifts€(from€lexicalÏimplication€rules).€€The€key€step€in€their€procedure€for€combining€temporary€graphs€is€a€maximalÏjoin€operation€formed€around€the€maximal€common€subgraph€using€the€most€specific€concepts€ofÏeach€graph.€€After€forming€a€graph€from€analysis€of€a€word's€definition,€they€search€theÏdictionary€for€places€where€that€word€is€used€in€defining€other€words;€this€information€isÏcombined€with€the€graphs€already€formed.€€While€these€clusters€are€similar€to€those€developedÏby€Microsoft,€they€are€based€on€more€rigorous€criteria€in€requiring€subsumption€relationshipsÏbetween€the€temporary€graphs€and€involve€use€of€only€semantically€significant€words.€€ThisÏinformation€is€useful€in€analyzing€the€entire€network€of€definitions€in€a€dictionary,€as€describedÏbelow€in€the€section€on€digraphs.ÌÌò òÝ ‚ê3 ÝÝ  ÝÝ‚ê3 wÝÔ2P¸ÔÚ  Ú7Ú  ÚÔ3  Ôà0  àÝ  ÝHigher€order€category€formationó ó݃ê3 w6wÝŒÐ~(#(# ÐŒÝ  ÝÌà  à€(Nida€1975)€indicates€that€a€semantic€domain€may€be€defined€based€on€any€semanticÏfeatures€associated€with€lexical€items.€€He€used€this€observation€to€assert€that€any€attempt€toÏidentify€a€single€hierarchy€or€ontology€was€somewhat€arbitrary€and€dependent€on€a€user's€need.€ÏProblems€with€direct€use€of€WordNet€synsets€in€information€retrieval€(q.v.€(Voorhees€1994))Ïmay€reflect€the€difficulty€in€using€a€single€hierarchy.ÌÌà  à€(Nida€1975:€174)€characterized€a€semantic€domain€as€consisting€of€words€sharingÏsemantic€components.€€(Litkowski€1997)€suggests€that€dynamic€category€systems€reflecting€moreÏof€the€underlying€features€and€semantic€components€of€lexical€entries€may€be€more€useful€inÏmany€NLP€applications,€thus€providing€importance€to€the€addition€of€this€information€whereverÏpossible.€€Several€techniques€have€been€developed€in€the€past€few€years€to€create€categorizationÏschemes€that€cut€across€the€static€WordNet€synsets.ÌÌò òÝ ‚ ©| ÝÝ  ÝÝ‚ ©|¶{ÝÔ2P¸ÔÚ  Ú7Ú  Ú.Ú  Ú1Ú  ÚÔ3  Ôà0  àÝ  ÝSupercategories€of€Hearstó ó݃ ©|¶{á{ÝŒÐ&'Å $(#(# ÐŒÝ  ÝÌà  à€(Hearst€&€SchðGðtze€1996)€provide€the€starting€point€for€creating€new€categories€out€ofÏWordNet€synsets.€€They€recognized€that€a€given€lexicon€may€not€suit€the€requirements€of€a€givenÏNLP€task€and€investigated€ways€of€customizing€WordNet€based€on€the€texts€at€hand.€€TheyÏadjusted€WordNet€in€two€ways:€€(1)€collapsing€the€fine„grained€structure€into€a€coarser€structure,Ð ´+S%) Ðbut€keeping€semantically„related€categories€together€and€letting€the€text€define€the€new€structureÏand€(2)€combining€categories€from€distant€parts€of€the€hierarchy.ÌÌà  àTo€collapse€the€hierarchy,€they€use€a€size€cutoff.€€They€formed€a€new€category€if€a€synsetÏhad€a€number€of€children€(hyponyms)€between€a€lower€and€upper€bound€(25€and€60€were€used).€ÏThey€formed€a€new€category€from€a€synset€if€it€had€a€number€of€hyponyms€greater€than€the€lowerÏbound,€bundling€together€the€synset€and€its€descendants.€€They€identified€726€categories€andÏused€these€as€the€basis€for€assigning€topic€labels€to€texts,€following€(Yarowsky€1992)€(collectingÏrepresentative€contexts,€identifying€salient€words€in€the€contexts€and€determining€a€weight€forÏeach€word,€and€predicting€the€appropriate€category€for€a€word€appearing€in€a€novel€context).€€ToÏextend€their€category€system,€they€computed€the€closeness€of€two€categories€based€on€co„¼ñëñ¼ñëñoccurrence€statistics€for€the€words€in€the€category€(using€large€corpora).€€They€then€used€theÏmutual€ranking€between€categories€(both€categories€had€to€be€highly€ranked€as€being€close€to€theÏother).€€As€a€result,€they€combined€the€original€726€categories€into€106€new€supercategories.€Ï(Names€for€the€new€supercategories€were€chosen€by€the€authors.)€€The€results€in€characterizingÏtexts€was€observably€better.€€They€also€noted€that€their€approach€could€be€used€at€a€narrowerÏlevel€in€order€to€achieve€greater€specificity.ÌÌò òÝ ‚ ©| ÝÝ  ÝÝ‚ ©|„ÝÔ2P¸ÔÚ  Ú7Ú  Ú.Ú  Ú2Ú  ÚÔ3  Ôà0  àÝ  ÝBasili€supercategoriesó ó݃ ©|„,„ÝŒÐÃb(#(# ÐŒÝ  ÝÌà  à€(Basili,€et€al.€1997)€describe€a€method€for€tuning€an€existing€word€hierarchy€(in€theirÏcase,€WordNet)€to€an€application€domain.€€The€technique€creates€new€categories€as€a€merging€ofÏWordNet€synsets€in€such€a€way€as€to€facilitate€elimination€of€particular€WordNet€senses,€thusÏreducing€ambiguity.ÌÌà  àThey€make€several€observations€about€the€nature€of€domain„specific€vocabularies.€€TheyÏnote€that€a€number€of€lexical€acquisition€techniques€become€more€viable€when€corpora€have€aÏdomain„specific€semantic€bias,€particularly€allowing€the€identification€of€domain„specificÏsemantic€classes.€€They€suggest€that€modeling€semantic€information€is€very€corpus€and€domainÏdependent,€and€general„purpose€sources€(MRDs€and€static€LKBs,€including€WordNet)€may€beÏtoo€generic.ÌÌà  àA€domain„specific€approach€can€take€advantage€of€several€findings:€€(1)€ambiguity€isÏreduced€in€a€specific€domain,€(2)€some€words€act€as€sense€primers€for€others,€and€(3)€rawÏcontexts€of€words€can€guide€disambiguation.€€They€use€a€classifier€that€tunes€WordNet€to€a€givenÏdomain,€with€the€resulting€classification€more€specific€to€the€sublanguage€and€then€able€to€beÏused€more€appropriately€to€guide€the€disambiguation€task.€€There€are€four€components€to€thisÏprocess:€€(1)€tuning€the€hierarchy€rather€than€attempting€to€select€the€best€category€for€a€word;Ï(2)€using€local€context€to€reduce€spurious€contexts€and€improve€reliability;€(3)€not€making€anyÏinitial€hypothesis€on€the€subset€of€consistent€categories€of€a€word;€and€(4)€considering€globallyÏall€contexts€to€compute€a€domain„specific€probability€distribution.ÌÐ ³+R%) Ðà  àTo€develop€the€classifier,€they€make€use€of€WordNet€tops€(unique€beginners)€as€classes.€ÏThey€first€compute€the€òòtypicalityóó€of€a€word€(to€which€class€does€most€of€a€word's€synsetsÐ Jé Ðbelong),€the€òòsynonymyóó€of€a€word€in€a€class€(the€number€of€words€in€the€corpus€appearing€in€atÐ 3Ò Ðleast€one€of€the€synsets€of€the€word€that€belong€to€the€class€divided€by€the€number€of€words€inÏthe€corpus€that€appear€in€at€least€one€of€the€synsets€of€the€word),€and€the€òòsaliencyóó€of€a€word€in€aÐ  ¤ Ðclass€(the€product€of€the€absolute€occurrences€of€the€word€in€the€corpus,€the€typicality,€and€theÏsynonymy).€€A€òòkernelóó€is€formed€for€a€class€by€selecting€words€with€a€high€saliency.€€This€kernelÐ × v Ðappears€to€be€clearly€distinctive€for€the€domain€(shown€in€the€example).ÌÌà  àIn€the€next€step,€the€kernel€words€are€used€to€build€a€probabilistic€model€of€a€class,€thatÏis,€distributions€of€class€relevance€of€the€surrounding€terms€in€typical€contexts€for€each€class€areÏbuilt.€€Then,€a€word€is€assigned€a€class€according€to€the€contexts€in€which€it€appears€in€order€toÏdevelop€a€òòdomain€senseóó.€€These€steps€reduce€the€WordNet€ambiguity€(from€3.5€to€2.2€in€theÐ Mì  Ðmaterial€presented).€€Finally,€each€word€is€assigned€a€class€based€on€maximizing€a€normalizedÏscore€of€the€domain€senses€over€the€set€of€kernel€words.ÌÌà  àThe€system€described€above€has€been€used€as€the€basis€for€inductively€acquiring€syntacticÏargument€structure,€selectional€restrictions€on€the€arguments,€and€thematic€assignments.€€ThisÏinformation€allows€further€clustering€of€the€senses,€which€would€enable€further€refinement€of€aÏcategory€system€like€WordNet,€that€is,€as€information€is€added€to€WordNet€entries,€all€the€stepsÏabove€could€be€performed€more€effectively.ÌÌò òÝ ‚ ©| ÝÝ  ÝÝ‚ ©|¨’ÝÔ2P¸ÔÚ  Ú7Ú  Ú.Ú  Ú3Ú  ÚÔ3  Ôà0  àÝ  ÝBuitelaar's€techniquesó ó݃ ©|¨’Ó’ÝŒÐg(#(# ÐŒÝ  ÝÌà  à€(Buitelaar€1997)€argues€that€a€lexical€item€should€be€assigned€a€representation€of€all€itsÏsystematically€related€senses,€from€which€further€semantic€processing€can€derive€discourseÏdependent€interpretations.€€This€type€of€representation€is€known€as€underspecification.€€In€thisÏcase,€it€is€based€on€the€development€of€systematic€polysemous€classes€with€a€class„basedÏacquisition€of€lexical€knowledge€for€specific€domains.€€The€general€approach€for€identifying€theÏclasses€stems€from€the€Generative€Lexicon€theory€of€(Pustejovsky€1995),€with€qualia€rolesÏenabling€type€coercion€for€semantic€interpretation.ÌÌà  àAn€important€basis€for€this€approach€is€disambiguation€between€senses€is€not€alwaysÏpossible€(the€problem€of€òòmultiple€referenceóó)€and€may€in€fact€not€be€appropriate,€since€anÐ k$ ! Ðutterance€may€need€to€convey€only€part€of€the€meaning€of€a€word,€without€requiring€specificationÏdown€to€a€final€nuance€(the€òòsense€enumerationóó€problem).€€One€may€think€of€representing€theÐ =&Ü# Ðdifferent€senses€of€a€word€in€its€own€hierarchy,€with€leaves€corresponding€to€fully„distinguishedÏsenses€and€with€internal€nodes€corresponding€to€decision€points€on€particular€semantic€features.€ÏThe€meaning€at€these€internal€nodes€is€thus€underspecified€for€the€semantic€features€at€the€leaves.ÌÌà  àBuitelaar€suggests€that€much€polysemy€is€systematic€and€uses€WordNet€classes€toÏidentify€the€systematicity.€€For€an€individual€word€with€multiple€WordNet€senses,€he€notes€thatÐ ³+R%) Ðthe€senses€may€group€together€on€the€basis€of€the€WordNet€tops€or€unique€beginners€and€thatÏeven€within€the€groups€the€senses€may€be€related€as€instantiations€of€particular€qualia€(òòformalóó,Ð Jé Ðòòconstitutiveóó,€òòtelicóó,€and€òòagentiveóó)€of€an€overarching€sense.Ð 3Ò ÐÌà  àBuitelaar€reduces€all€of€WordNet's€sense€assignments€to€a€set€of€32€basic€sensesÏ(corresponding€to,€but€not€exactly€identical€to,€WordNet's€26€tops).€€He€identifies€442Ïpolysemous€classes€in€WordNet,€each€of€which€is€induced€by€words€having€more€that€one€top.€ÏSome€of€these€do€not€correspond€to€systematic€polysemy,€but€are€rather€derived€from€homonymsÏthat€are€ambiguous€in€similar€ways€and€that€hence€are€eliminated€from€further€study.ÌÌà  àQualia€roles€are€typed€to€a€specific€class€of€lexical€items.€€The€types€are€simple€(ò òhumanó ó,Ð {  Ðò òartifactó ó)€or€complex€(ò òinformationð"ðphysicaló ó),€also€called€"dotted€types."€€There€are€two€complexÐ g  Ðtypes:€€(1)€systematically€related€(where€an€utterance€simultaneously€and€necessarilyÏincorporates€both€of€the€simple€types€of€which€it€is€composed,€e.g.,€òòbookóó,€òòjournalóó,€òòscoreboardóó€areÐ <Û  Ðò òinformationó ó€and€ò òphysicaló ó€at€the€same€time,€a€"closed€dot")€and€(2)€related€but€notÐ %Ä  Ðsimultaneously€(only€one€aspect€is€(usually)€true€in€an€utterance,€e.g.,€òòfishóó€is€ò òanimalð!ðfoodó ó,€but€isÐ °  Ðonly€one€of€these€in€a€given€utterance,€an€"open€dot").€€Open„dot€types€generally€seem€toÏcorrespond€to€systematic€polysemy,€such€as€induced€by€the€òòanimal„grindingóó€lexical€relation.€Ð æ… ÐIdentification€of€such€lexical€relations€is€still€an€open€area€of€research.ÌÌà  àThe€underspecified€types€enumerated€above€can€be€adapted€to€domain„specific€corpora.€ÏThe€underspecified€type€is€a€basic€lexical„semantic€structure€into€which€specific€information€forÏeach€lexical€item€can€be€put,€that€is,€provides€variables€which€can€be€instantiated.€€BuitelaarÏsuggests€that€the€manner€of€instantiation€is€domain„€and€corpus„specific.€€He€first€tags€each€wordÏin€a€corpus€with€the€underspecified€type.€€The€next€step€involves€pattern„matching€on€generalÏsyntactic€structures,€along€with€heuristics€to€determine€whether€a€specific€type€is€appropriate€forÏthe€application€of€the€pattern.€€For€example,€the€pattern€"NP€Prep€NP",€where€Prep€=€"of",Ïindicates€a€"part„whole"€relation€if€the€head€noun€of€the€first€NP€has€a€type€either€the€same€asÏthat€of€the€second€NP€or€is€one€of€the€composing€types€of€the€second€NP.€€Thus,€"the€secondÏparagraph€of€a€journal,"€with€"paragraph"€of€type€ò òinformationó ó€and€"journal"€of€typeÐ Ò q Ðò òinformationð"ðphysicaló ó,€allows€the€inference€that€the€"paragraph"€is€a€part€of€the€"journal."Ð ¾!] ÐÌà  àThe€information€gathered€in€the€second€step€is€used€to€classify€unknown€words.€€ResultsÏof€the€classifier€seem€to€relate€to€the€homogeneity€of€the€corpus.€€Finally,€the€underspecifiedÏlexicon€is€adapted€to€a€specific€domain€by€using€the€observed€patterns€and€translating€them€intoÏsemantic€ones€and€generating€a€semantic€lexicon€representing€that€information.€€ParticularÏpatterns€are€viewed€as€identifying€hypernyms€(the€formal€quale),€meronyms€(the€constitutiveÏquale),€and€predicate„argument€structure€(the€telic€and€agentive€qualia).Ìâ âÐ  )¨"& Ðò òÔ& Ž Ôâ âÝ ‚ ©| ÝÝ  ÝÝ‚ ©|P§ÝÔ2P¸ÔÚ  Ú7Ú  Ú.Ú  Ú4Ú  ÚÔ3  Ôà0  àÝ  ÝIntersective€setsó ó݃ ©|P§{§ÝŒÐa(#(# ÐŒÝ  ÝÌà  à€(Palmer,€et€al.€1997)€are€concerned€with€lexical€acquisition€and€have€described€anÏimplementation€of€lexical€organization€that€may€have€increased€potential€for€adaptable€lexicalÏÔ'Ža:§Ôprocessing.€€They€explicitly€represent€a€lexical€hierarchy€that€captures€fine„grained€classes€ofÏlexical€items,€as€well€as€their€associations€with€other€classes€that€share€similar€semantic€andÏsyntactic€features.€€This€approach€is€being€applied€to€the€Lexicalized€Tree€Adjoining€Grammar.€ÏThey€hypothesize€that€syntactic€frames€can€be€used€to€extend€verb€meanings€and€thus€acquireÏnew€senses€for€lexical€items.ÌÌà  à€(Levin€1993)€verb€classes€are€based€on€regularities€in€diathesis€alternations,€as€specifiedÏby€several€pairs€of€syntactic€frames.€€There€is€an€underlying€hypothesis€that€these€classesÏcorrespond€to€some€underlying€semantic€components,€which€are€discussed€in€general€terms€butÏnot€yet€made€explicit.€€For€an€unknown€verb€in€a€text,€being€able€to€recognize€its€syntacticÏpattern€provides€a€reasonable€prediction€of€its€verb€class,€thus€providing€a€first€attempt€toÏcharacterize€its€semantic€features.€€This€may€sometimes€enable€a€sense€extension€for€an€existingÏverb.ÌÌà  àPalmer,€et€al.€have€examined€Levin's€verbs€in€conjunction€with€the€WordNet€synsets.€€InÏparticular,€they€observed€that€many€verbs€fall€into€multiple€Levin€classes.€€They€augmentedÏLevin€classes€with€so„called€òòintersectiveóó€classes,€grouping€existing€classes€that€share€at€leastÐ –5 Ðthree€members,€with€the€hypothesis€that€such€an€overlap€might€correspond€to€a€systematicÏrelationship.€€The€intersective€class€names€consist€of€the€Levin€class€numbers€from€which€theyÏwere€formed.€€(Since€Levin€includes€only€4,000€verbs,€with€20,000€identified€in€a€largeÏdictionary,€each€set€may€conceivably€be€extended,€allowing€reapplication€of€this€technique.€€TheÏanalysis€could€also€be€extended€to€overlaps€containing€only€two€members.)€€Palmer,€et€al.Ïidentified€129€intersective€classes;€they€then€reclassified€the€verbs,€removing€them€from€theÏLevin€classes€if€they€occurred€in€an€intersective€class.€€This€reduced€the€"ambiguity"€of€the€verbsÏ(that€is,€the€number€of€classes€to€which€a€verb€belongs).€€Moreover,€the€resulting€intersectiveÏclasses€had€face€validity,€seeming€to€correspond€to€intuitively€apparent€idiosyncratic€ambiguities.ÌÌà  àAs€mentioned€above,€the€Levin€classes,€even€though€capturing€common€syntacticÏpatterning,€are€thought€to€correspond€to€semantic€differences.€€So,€the€intersective€classes€wereÏexamined€in€conjunction€with€WordNet€synsets.€€Although€the€analysis€was€performed€mostly€byÏhand€and€with€intuitive€judgments,€the€comparison€apparently€is€made€by€identifying€WordNetÏsynsets€that€have€hyponyms€in€the€intersective€class€and€the€two€classes€from€which€it€wasÏformed.€€Thus,€with€the€intersective€class€"cut/split,"€it€was€possible€to€identify€WordNetÏdistinctions€of€synsets€"cut€into,€incise"€and€"cut,€separate€with€an€instrument"€(andÏcoincidentally,€indicating€that€the€first€of€these€synsets€is€a€hyponym€of€the€second).ÌÌà  àPalmer,€et€al.€indicate€that€they€are€building€frames€to€represent€the€meanings€of€theirÏlexical€entries,€capturing€syntactic€and€semantic€distinctions.€€By€examining€the€relationships€ofÐ ³+R%) Ðthese€entries€with€the€information€obtained€from€the€intersective€class€analysis€and€the€WordNetÏsynsets,€they€can€more€easily€identify€the€specific€syntactic€and€semantic€distinctions€(that€is,Ïdisambiguate€one€class€with€another€and€vice€versa).€€Moreover,€it€then€becomes€easier€toÏarrange€the€lexical€items€into€an€inheritance€hierarchy€where€specific€syntactic€and€semanticÏcomponents€are€expressed€as€templates.ÌÌà  àBased€on€the€inheritance€hierarchy,€they€can€then€measure€the€proximity€of€classes€in€theÏlattice€in€terms€of€the€degree€of€overlap€between€each€class's€defining€features.€€Conversely,€butÏnot€mentioned€by€the€authors,€it€seems€possible€to€go€the€other€way.€€If€lexical€entries€have€aÏbundle€of€syntactic€and€semantic€features,€they€can€be€examined€for€common€components€toÏidentify€templates€(e.g.,€containing€a€field€for€number€with€a€set€of€possible€values).ÌÌò òÝ ‚ ©| ÝÝ  ÝÝ‚ ©|º¸ÝÔ2P¸ÔÚ  Ú7Ú  Ú.Ú  Ú5Ú  ÚÔ3  Ôà0  àÝ  ÝAbstractionó ó݃ ©|º¸å¸ÝŒÐMì (#(# ÐŒÝ  ÝÌà  àAbstraction€is€the€process€of€identifying€these€underlying€features€and€relaxing€andÏremoving€the€subsidiary€features€to€create€a€more€general€characterization€of€a€set€of€words€or€aÏtext.€€(Litkowski€&€Harris€1997;€Litkowski€1997)€describe€principles€and€procedures€forÏcategory€development,€particularly€noting€the€similarity€to€(Hearst€&€SchðGðtze€1996)€in€providingÏsupercategories.€€A€general€theme€in€these€principles€and€procedures€was€the€importance€ofÏcharacterizing€lexical€entries€in€terms€of€their€syntactic€and€semantic€features.€€Another€themeÏwas€that€existing€categorizations,€such€as€WordNet,€should€not€be€viewed€as€static€entities.€€ThisÏstems€not€from€the€fact€that€one€may€quibble€with€WordNet€entries€and€hierarchies,€but€ratherÏfrom€the€hypothesis€that€characterization€of€a€categorization€scheme€or€a€text€may€cut€acrossÏWordNet€synsets€because€the€characterization€involves€highlighting€of€different€underlyingÏsyntactic,€semantic,€or€other€lexical€features.ÌÌà  à€(Litkowski€&€Harris€1997)€particularly€dealt€with€category€development€for€textualÏmaterial,€that€is,€characterizing€the€discourse€structure€of€a€text.€€There,€a€discourse€analysis€wasÏperformed€generally€following€Allen's€algorithm€for€managing€the€attentional€stack€in€discourseÏstructure€analysis€(Allen€1995:€526-9),€with€an€extension€to€incorporate€lexical€cohesionÏprinciples€(Halliday€&€Hasan€1976).€€The€algorithms€involved€identifying€discourse€segments,Ïdiscourse€entities,€local€discourse€contexts€(for€anaphora€resolution),€and€eventualities.€€TheÏresult€was€a€set€of€discourse€segments€related€to€one€another€(with€many€identified€asÏsubsidiary),€discourse€entities€and€eventualities,€and€various€role€and€ontological€relationsÏbetween€these€entities.€€The€concepts€and€relations€(including€the€discourse€relations)€wereÏessentially€present€in€and€licensed€by€the€lexicon,€and€then€instantiated€by€the€given€text€to€carveÏout€a€subnetwork€of€the€lexicon.€€The€definition€of€this€subnetwork€was€then€constructed€byÏidentifying€the€highest€nodes€in€the€ISA€backbone€and€the€additional€relations€that€operate€on€theÏbackbone,€along€with€selectional€restrictions€that€are€used.ÌÌà  àCharacterizing€this€subnetwork€was€a€matter€of€identifying€the€topmost€ISA€nodes€(andÏperhaps€more€importantly,€identifying€descendants€that€to€be€excluded).€€Naming€thisÐ ³+R%) Ðsubnetwork€is€based€on€the€set€of€topmost€nodes,€any€relations€(semantic€roles€or€other€semanticÏrelations),€and€selectional€restrictions.€€This€process€of€characterizing€a€subnetwork€is€quiteÏsimilar€to€the€development€of€supercategories€in€.€€Thus,€to€at€least€that€extent,€this€process€mayÏbe€viewed€as€leading€to€identification€of€the€topic€of€a€text.€€(It€is€assumed€that€the€network€nodesÏare€organized€in€the€same€way€as€WordNet€synsets,€that€is,€several€lemmas€expressing€the€sameÏconcept.€€This€would€constitute€a€thematic€characterization€of€a€text.€€The€exclusion€ofÏdescendants€would€perhaps€increase€precision€in€information€retrieval,€a€significant€problemÏwith€search€engines€that€allow€thesaural€substitutions€or€expand€queries€based€on€themes.)ÌÌò òÝ ‚ê3 ÝÝ  ÝÝ‚ê3ÆÝÔ2P¸ÔÚ  Ú8Ú  ÚÔ3  Ôà0  àÝ  ÝExtension€of€lexical€entriesó ó݃ê3Æ1Æ݌В1 (#(# ÐŒÝ  ÝÌà  àAn€important€characteristic€of€a€lexicon€is€that€the€entries€and€senses€are€frequentlyÏsystematically€related€to€one€another.€€Many€lexical€entries€are€derived€from€existing€ones.€ÏLexical€rules€can€cover€a€variety€of€situations:€€derivational€morphological€processes,€change€ofÏsyntactic€class€(conversion),€argument€structure€of€the€derived€predicate,€affixation,€andÏmetonymic€sense€extensions.€€Thus,€lexical€rules€should€"express€sense€extension€processes,€andÏindeed€derivational€ones,€as€fully€productive€processes€which€apply€to€finely€specified€subsets€ofÏthe€lexicon,€defined€in€terms€of€both€syntactic€and€semantic€properties€expressed€in€the€typeÏsystem€underlying€the€organization€of€the€lexicon"€(Copestake€&€Briscoe€1991).€€The€most€basicÏof€these€derivational€relations€is€the€one€in€which€inflected€forms€are€generated.€€These€areÏgenerally€quite€simple,€and€include€the€formation€of€plural€forms€of€nouns,€the€formation€ofÏtensed€(past,€past€participle,€gerund)€forms€of€verbs,€and€the€formation€of€comparative€andÏsuperlative€forms€of€adjectives.ÌÌà  àDerivational€relations€may€form€verbs€from€nouns,€nouns€from€verbs,€adjectives€fromÏnouns€and€verbs,€nouns€from€adjectives,€and€adverbs€from€adjectives.€€Many€of€these€relationsÏhave€morphological€implications,€with€the€addition€of€prefixes€and€suffixes€to€base€forms.€€TheseÏrelations€generally€operate€at€the€level€of€the€lexical€entries.ÌÌà  àIn€a€lexicon€where€entries€are€broken€down€into€distinct€senses,€the€senses€may€beÏsystematically€related€to€one€another€without€any€morphological€consequences.€€The€òòanimal„Ð °!O ÐñëñÐ °!O Ðñëñgrindingóó€lexical€relation€mentioned€above€is€such€an€example.Ð ™"8 ÐÌà  àThe€status€of€lexical€relations€is€currently€undergoing€substantial€refinement€(seeÏ(Helmreich€&€Farwell€1996)€for€example).€€Several€useful€developments€have€recently€occurredÏthat€have€implications€for€the€content€of€lexical€entries€themselves.ÌÌò òÝ ‚ ©| ÝÝ  ÝÝ‚ ©|ÒÎÝÔ2P¸ÔÚ  Ú8Ú  Ú.Ú  Ú1Ú  ÚÔ3  Ôà0  àÝ  ÝInstantiation€of€lexical€rulesó ó݃ ©|ÒÎýÎÝŒÐ(®!%(#(# ÐŒÝ  ÝÌà  à€(Flickinger€1987)€first€introduced€the€notion€that€lexical€rules€were€important€parts€of€aÏhierarchical€lexicon.€€(Copestake€&€Briscoe€1991)€describe€types€of€noun€phrase€interpretationsÏthat€may€involve€metonymy:€€individual„denoting€NPs,€event„denoting€NPs€(subdivided€intoÐ ´+S%) Ðthose€with€telic€roles€and€those€with€agentive€roles,€based€on€an€underspecified€predicate),Ïanimal„denoting€interpretation€vs.€food„denoting€one,€count€nouns€transformed€into€mass€sensesÏdenoting€a€substance€derived€from€the€object.€€Perhaps€as€important€as€describing€theseÏprocesses,€Copestake€and€Briscoe€also€were€able€to€express€these€lexical€rules€as€lexical€entriesÏthemselves€(in€a€typed€feature€structure).€€(These€might€be€called€"pseudoentries"€to€distinguishÏthem€from€words€and€phrases€that€would€be€used€in€texts.)ÌÌà  àThe€essence€of€the€representation€is€that€a€lexical€rule€consists€of€two€featuresÏ(denominated€<0>€and€<1>),€where€the€first€feature€(<0>)€has€a€value€(which€is€itself€a€complexÏfeature€structure)€that€specifies€the€typed€feature€structures€to€be€matched€and€the€second€featureÏ(<1>)€has€a€value€that€specifies€the€typed€feature€structure€in€the€derived€entry€or€sense€(where,Ïfor€example,€a€new€value€for€an€"orthography"€feature€would€create€a€new€entry€in€the€lexicon).ÌÌà  àThis€representational€formalism€could€be€used€to€extend€a€lexicon.€€One€could€take€anÏexisting€lexicon€and€start€a€process€to€generate€new€entries€and€senses€for€each€lexical€rule.€€ThisÏprocess€would€simply€iterate€through€a€list€of€rules,€find€any€entries€and€senses€to€which€the€<0>Ïfeature€applies,€and€create€new€entries€and€senses€based€on€the€<1>€feature€of€the€lexical€rule.€ÏConversely,€in€a€recognition€system,€for€any€unknown€word€or€use€of€an€existing€word,€oneÏcould€create€a€tentative€entry€or€sense€(postulating€various€syntactic€and€semantic€features),Ïsearch€the€lexical€rules€to€determine€if€any€of€them€has€a€<1>€feature€matching€the€postulatedÏentry€or€sense,€and€then€determine€if€the€corresponding€<0>€feature€matches€an€existing€entry€orÏsense€(thus€validating€the€characterization€of€the€unknown€word€or€sense).ÌÌò òÝ ‚ ©| ÝÝ  ÝÝ‚ ©|SØÝÔ2P¸ÔÚ  Ú8Ú  Ú.Ú  Ú2Ú  ÚÔ3  Ôà0  àÝ  ÝProbabilistic€Finite€State€Machines€in€Lexical€Entriesó ó݃ ©|SØ~ØÝŒÐPï(#(# ÐŒÝ  ÝÌà  à€(Briscoe€&€Copestake€1996)€recognize€various€efficiency€issues€that€have€arisen€inÏconnection€with€systems€that€rely€heavily€on€lexical€rules.€€They€note€the€development€ofÏtechniques€for€(1)€'on„demand'€evaluation€of€lexical€rules€at€parse€time,€(2)€the€storage€of€finiteÏstate€machines€in€lexical€entries€to€identify€possible€"follow€relations"€(an€ordering€of€lexicalÏrules€that€can€apply€to€a€lexical€entry),€and€(3)€an€extension€of€entries€with€information€commonÏto€all€their€derived€variants.€€Notwithstanding,€they€state€that€"neither€the€interpretation€of€lexicalÏrules€as€fully€generative€or€as€purely€abbreviatory€is€adequate€linguistically€or€as€the€basis€forÏLKBs."ÌÌà  àTo€deal€with€this€problem,€they€create€a€notion€of€probabilistic€lexical€rules€to€correspondÏwith€language€users'€assessments€of€the€degree€of€acceptability€of€a€derived€form.€€TheyÏintroduce€probabilities€in€both€the€lexical€entries€and€the€lexical€rules.€€For€the€lexical€entries,Ïthey€assume€a€finite€state€machine€that€can€represent€the€possible€application€of€lexical€rules,Ïwhich€are€intended€to€encompass€all€entry€and€sense€derivations€from€a€base€form.€€This€is€theÏconditional€probability€of€a€lexical€entry€of€the€given€sense€given€the€word€form€(the€frequencyÏof€the€derived€form,€e.g.,€a€particular€subcategorization€pattern,€divided€by€the€frequency€of€theÏword€form).€€Some€states€will€have€no€associated€probability€if€they€are€not€attested.€€There€is,€ofÐ ³+R%) Ðcourse,€the€difficulty€of€acquiring€reliable€estimates,€and€they€note€the€desirability€of€usingÏsmoothing€techniques€for€rare€words.ÌÌà  àFor€unattested€derived€lexical€entries,€the€relative€productivity€of€the€lexical€rule€can€beÏused.€€To€compute€this,€they€identify€all€the€forms€to€which€the€rule€can€apply€and€thenÏdetermine€how€often€it€is€used.€€(For€example,€they€would€determine€how€often€the€lexical€ruleÏtransforming€òòvehicleóó€into€òògo€using€vehicleóó,€Levin's€class€51.4.1,€occurs.€€They€would€thenÐ × v Ðdetermine€from€a€noun€hierarchy€all€nouns€that€identify€vehicles)Ô% €+ Ô€€ÌÌò òÝ ‚ ©| ÝÝ  ÝÝ‚ ©|ºáÝÔ2P¸ÔÚ  Ú8Ú  Ú.Ú  Ú3Ú  ÚÔ3  Ôà0  àÝ  ÝPhrase€variationó ó݃ ©|ºáåá݌В1 (#(# ÐŒÝ  ÝÌà  àIdioms€and€phrases€(multi„word€terms)€constitute€a€significant€problem€in€lexiconÏdevelopment.€€This€is€an€area€in€which€many€developments€are€emerging.€€There€is€a€spectrum€ofÏnon„random€cooccurrences€in€language,€loosely€called€collocations,€that€may€be€said€to€rangeÏfrom€syntactic€patterns€to€specific€word€combinations€that€must€appear€exactly€in€sequence€andÏwhose€meaning€is€not€composed€from€the€meanings€of€its€constituent€words.€€At€this€latter€end€ofÏthe€spectrum,€the€word€combinations€achieve€the€status€of€constituting€a€distinct€lexical€entry.€ÏThe€dividing€line€between€what€constitutes€a€lexical€entry€is€not€clearly€drawn.€€The€issue€of€howÏto€recognize€the€word€combinations€is€also€not€yet€firmly€established.ÌÌà  à€(Mel'ðcðuk€&€Zholkovsky€1988)€describe€many€functional€relations€that€may€give€rise€toÐ –5 Ðcollocations.€€(Smadja€&€McKeown€1990)€categorized€collocations€as€open€compounds,Ïpredicative€relations,€and€idiomatic€expressions.€€(Smadja€&€McKeown€1991)€describeÏprocedures€for€lexical€acquisition€of€multi„word€terms€and€their€variations.€€Generally,€theseÏprocedures€have€been€useful€for€proper€nouns,€particularly€organizations€and€company€names.€ÏSome€recent€developments€suggest€that€a€broadened€view€of€the€lexicon,€its€structure,€and€theÏcontents€of€its€entries€may€be€useful€in€the€further€characterization€of€multi„word€terms.ÌÌà  à€(Burstein,€et€al.€1996;€Burstein,€et€al.€1997)€developed€domain„specific€conceptÏgrammars€which€correspond€to€the€inverse€of€the€variant€extension€technique€described€forÏlexical€rules.€€These€grammars€were€used€to€classify€15„€to€20„word€phrases€€and€essays€(answersÏto€test€items)€for€use€in€an€automatic€scoring€program.€€Automatic€scoring€must€be€able€toÏrecognize€paraphrased€information€across€essay€responses€and€to€identify€similar€words€inÏconsistent€syntactic€patterns,€as€suggested€by€(Montemagni€&€Vanderwende€1993).ÌÌà  àThey€built€a€concept€lexicon€identifying€words€thought€to€convey€the€same€conceptÏ(using€only€the€relevant€vocabulary€in€a€set€of€training€responses).€€They€parsed€the€answersÏ(using€the€Microsoft€parser),€and€substituted€superordinate€concepts€from€the€lexicon€for€wordsÏin€the€parse€tree.€€They€then€extract€the€phrasal€nodes€containing€these€concepts.€€In€the€finalÏstage,€phrasal€and€clausal€constituents€are€relaxed€into€a€generalized€representation€(XP,€ratherÏâ âthan€NP,€VP,€or€AP).€€Their€concept€grammars€for€classifying€answers€were€then€formed€on€theÐ Ê*i$( Ðbasis€of€the€generalized€representation.€€In€part,€these€concept€grammars€are€licensed€by€the€factÏthat€many€concepts€are€realized€in€several€parts€of€speech.Ìâ âÌà  à€(Jacquemin,€et€al.€1997)€describe€a€system€for€automatic€production€of€index€terms€toÏachieve€greater€coverage€of€multi„word€terms€by€incorporating€derivational€morphology€andÏtransformational€rules€with€their€lexicon.€€This€is€a€domain€independent€system€for€automaticÏterm€recognition€from€unrestricted€text.€€The€system€starts€with€a€list€of€controlled€terms,Ïautomatically€adds€morphological€variants,€and€considers€syntactic€ways€linguistic€concepts€areÏexpressed.ÌÌà  àThey€identify€three€major€types€of€linguistic€variation:€€(1)€syntactic€(the€content€wordsÏare€found€in€a€variant€syntactic€structure,€e.g.,€òòtechnique€for€performing€volumetricÐ d  Ðmeasurementsóó€is€a€variant€of€òòmeasurement€techniqueóó);€(2)€morpho„syntactic€(the€content€wordsÐ Mì  Ðor€derivational€variants€are€found€in€a€different€syntactic€structure,€e.g.,€òòelectrophoresed€on€aÐ 6Õ  Ðneutral€polyacrylamide€gelóó€is€a€variant€of€òògel€electrophoresisóó);€and€(3)€semantic€(synonyms€areÐ ¾  Ðfound€in€the€variant,€e.g.,€òòkidney€functionóó€is€a€variant€of€òòrenal€functionóó).€€The€morphologicalÐ §  Ðanalysis€is€more€elaborate€than€simple€stemming.€€First,€inflectional€morphology€is€performed€toÏget€the€different€analyses€of€word€forms.€€Next,€a€part€of€speech€tagger€is€applied€to€the€text€toÏperform€morphosyntactic€disambiguation€of€words,€€Finally,€derivational€morphology€is€appliedÏ(over)generate€morphological€variants.€€This€overgeneration€is€not€a€problem€because€the€termÏexpansion€process€and€collocational€filtering€will€avoid€incorrect€links.ÌÌà  àThe€next€phase€deals€with€transformation„based€term€expansion.€€Transformations€areÏinferred€from€the€corpus€based€on€linguistic€variations€(distinct€from€morphological€variants).€ÏTwo€general€types€of€variation€are€identified:€€(1)€variations€based€on€syntactic€structure:€(a)Ïcoordination€(òòchemical€and€physical€propertiesóó€is€a€variation€of€òòchemical€propertiesóó),€(b)Ð "Á Ðsubstitution/modification€(òòprimary€cell€culturesóó€is€a€variation€of€òòcell€culturesóó),€(c)Ð  ª Ðcompounding/decompounding€(òòmanagement€of€the€wateróó€is€a€variation€of€òòwater€managementóó)Ð ô“ Ðand€(2)€variations€according€to€the€type€of€morphological€variation:€€(a)€noun„noun€variations,Ï(b)€noun„verb€variations€(òòinitiate€budsóó€is€a€variation€of€òòbud€initiationóó),€and€(c)€noun„adjectiveÐ Æ e Ðvariations€(òòionic€exchangeóó€is€a€variation€of€òòion€exchangeóó).€€A€grammar€(a€set€of€metarules)€wasÐ ¯!N Ðdevised€to€serve€as€the€basis€for€filtering,€using€only€regular€expressions€to€identify€permissibleÏtransformations.ÌÌò òÝ ‚ ©| ÝÝ  ÝÝ‚ ©|$øÝÔ2P¸ÔÚ  Ú8Ú  Ú.Ú  Ú4Ú  ÚÔ3  Ôà0  àÝ  ÝUnderspecified€formsó ó݃ ©|$øOøÝŒÐS%ò"(#(# ÐŒÝ  ÝÌà  àThe€reverse€of€lexical€extension€through€lexical€rules€leads€to€the€notion€ofÏunderspecified€forms.€€As€mentioned€earlier,€(Buitelaar€1997)€suggested€a€notion€ofÏunderspecification€in€the€identification€of€categories.€€(Sanfilippo€1995)€presented€an€approach€toÏlexical€ambiguity€where€sense€extension€regularities€are€represented€by€underspecifyingÏâ âmeanings€through€lexical€polymorphism.€€He€particularly€cited€verb€alternations€(Levin€1993)Ð Ê*i$( Ðand€qualia€structures€(Pustejovsky€1995)€and€suggested,€since€there€is€no€control€on€theÏapplication€of€lexical€rules,€the€use€of€underspecified€forms.Ìâ âÌà  àSanfilippo€proposed€to€represent€ambiguities€arising€from€multiple€subcategorizationsÏusing€"polymorphic"€subcategorization€lexical€entries€with€a€typed„feature„structureÏformalization.€€An€entry€is€created€to€represent€all€possible€subcategorizations€and€then€syntacticÏcontextual€information€is€used€during€language€processing€to€identify€(or€ground)€theÏunderspecified€form€(binding€particular€variables).€€This€was€done€by€generating€a€list€ofÏresolving€clauses€(in€Prolog)€which€identify€how€the€terminal€types€are€inferred€from€specificÏcontextual€information.€€Moreover,€he€noted€that€the€resolving€clauses€could€themselves€beÏpositioned€within€a€thematic€type€hierarchy€so€that€it€would€be€unnecessary€for€this€informationÏto€be€specified€within€each€lexical€entry,€allowing€it€to€be€inherited.€€Considerable€research€isÏpresently€under€way€to€extend€the€notion€of€underspecification.ÌÌò òÝ ‚ê3 ÝÝ  ÝÝ‚ê3.ÿÝÔ2P¸ÔÚ  Ú9Ú  ÚÔ3  Ôà0  àÝ  ÝDigraph€theory€techniquesó ó݃ê3.ÿYÿ݌о (#(# ÐŒÝ  ÝÌà  à€(Litkowski€1975;€Litkowski€1976;€Litkowski€1978;€Litkowski€1980)€studied€theÏsemantic€structure€of€paper€dictionaries€as€labeled€directed€graphs€(digraphs)€in€an€overall€effortÏto€identify€semantic€primitives.€€In€these€studies,€the€starting€point€was€to€view€nodes€in€theÏdigraphs€as€entries€(and€later€as€concepts)€and€arcs€as€definitional€relations€between€entriesÏ(initially€the€simple€relation€"is€used€to€define"€and€later€as€the€various€types€of€semanticÏrelations).€€Digraph€theory€allows€predictions€about€the€semantic€structure.€€In€particular,€itÏasserts€that€every€digraph€has€a€point€basis€(that€is,€primitives)€from€which€every€point€in€theÏdigraph€may€be€reached.€€It€provides€a€rationale€for€moving€toward€those€primitives€(theÏdevelopment€of€"reduction€rules"€that€allow€the€elimination€of€words€and€senses€as€non„¼ñëñ¼ñëñprimitive).€€It€makes€a€prediction€that€primitive€concepts€are€concepts€that€can€be€verbalized€andÏlexicalized€in€several€ways.€€(These€predictions€were€well€served€in€the€development€ofÏWordNet,€where€unique€beginners€were€identified€as€consisting€of€several€words€and€phrases,Ïthat€is,€the€synsets.€€Whether€analysis€of€dictionary€definitions€in€an€unabridged€would€yield€theÏsame€set€is€an€open€question.)ÌÌà  à€(Richardson€1997)€commented€on€the€"problems€with€circularity€and€inconsistency€...€inÏthe€resulting€hierarchies"€noted€in€earlier€studies€(Amsler€1980;€Chodorow,€et€al.€1985;€Ide€&ÏVeronis€1993).€€He€states€that€the€massive€network€built€at€Microsoft€invalidates€this€criticism.€ÏHowever,€he€did€not€examine€this€network€to€determine€if€it€contained€any€circularities€orÏinconsistencies.€€(Litkowski€1978)€and€(Barrið/ðre€&€Popowich€1996a)€discussed€this€problem,Ïwith€the€latter€noting€that,€for€a€well„constructed€children's€dictionary,€with€a€relatively€smallÏnumber€of€definitions,€the€"taxonomy€is€a€forest€with€multiple€trees,€each€of€which€having€at€itsÏroot€a€group€of€words€defined€through€a€loop"€containing€a€group€of€synonyms.€€The€results€fromÏthe€study€of€digraphs,€along€with€the€techniques€of€Barriere,€suggest€that€Microsoft's€MindNetÏcan€be€subjected€to€further€analysis€to€organize€the€sets€of€structures.ÌÐ ³+R%) Ðà  àThe€digraph€techniques€further€substantiate€the€notion€of€lexical€underspecification.€ÏWhen€the€definition€of€a€node€is€expanded€from€representing€an€entry€to€representing€theÏconcepts€in€the€senses,€several€observations€immediately€come€into€play.€€The€first€is€that€theÏsenses€themselves€should€be€organized€into€their€own€hierarchy.€€The€second€is€that€nodes€in€theÏsense€hierarchy€frequently€correspond€to€the€common€factors€of€the€subsenses.ÌÌò òÝ ‚ê3 ÝÝ  ÝÝ‚ê3“ ÝÔ2P¸ÔÚ  Ú10Ú  ÚÔ3  Ôà0  àÝ  ÝConclusionsó ó݃ê3“ ¾ ÝŒÐ× v(#(# ÐŒÝ  ÝÌà  àPopulation€and€propagation€of€information€throughout€an€LKB€is€a€valuable€enterprise.€ÏIt€is€intellectually€stimulating€in€its€own€right,€providing€many€insights€into€the€ways€in€whichÏhumans€structure€concepts€and€knowledge.€€More€importantly,€the€use€of€the€techniquesÏdescribed€provides€mechanisms€for€filling€out€information€that€can€be€used€in€many€applications.€ÏThe€techniques€suggest€that€the€more€information€contained€in€the€LKB,€the€greater€the€numberÏof€applications€that€might€make€use€of€the€information€in€novel€ways.€€The€techniquesÏthemselves€may€be€useful€in€these€applications.€€Many€of€the€techniques€involve€bootstrappingÏoperations,€so€that€the€evolution€of€the€LKB€and€its€use€can€begin€small€and€grow€incrementally.€ÏFinally,€these€techniques€and€information€can€be€used€in€developing€lexical€acquisitionÏprocedures€to€obtain€external€information.€€Together,€the€internal€lexicon€computations€and€theirÏapplication€to€external€methods€may€contribute€greatly€to€solving€the€bottleneck€problem.ÌÌà@%ìàò òReferencesó óˆÐ –5 ÐÌà0  àà ° àAllen,€J.€(1995).€òòNatural€language€understandingóó€(2nd).€Redwood€City,€CA:€TheÐ i ÐBenjamin/Cummings€Publishing€Company,€Inc.Ð (#(# Ðà0  àà ° àòòThe€American€Heritage€Dictionary€of€the€English€Languageóó€(A.€Soukhanov,€Ed.)€(3rd).€(1992).Ð ;Ú ÐBoston,€MA:€Houghton€Mifflin€Company.Ð (#(# Ðà0  àà ° àAmsler,€R.€A.€(1980).€The€structure€of€the€Merriam-Webster€pocket€dictionary€[Diss],€Austin:ÏUniversity€of€Texas.Ð (#(# Ðà0  àà ° àBarrið/ðre,€C.,€&€Popowich,€F.€(1996a).€Building€a€noun€taxonomy€from€a€children's€dictionary.ÏEURALEX96.€Gð=ðteborg,€Sweden.Ð (#(# Ðà0  àà ° àBarrið/ðre,€C.,€&€Popowich,€F.€(1996b).€Concept€clustering€and€knowledge€integration€from€aÏchildren's€dictionary.€COLING96.Ð (#(# Ðà0  àà ° àBasili,€R.,€Rocca,€M.€D.,€&€Pazienza,€M.€T.€(1997).€Towards€a€bootstrapping€framework€forÏcorpus€semantic€tagging.€4th€Meeting€of€the€ACL€Special€Interest€Group€on€the€Lexicon.ÏWashington,€DC:€Association€for€Computational€Linguistics.Ð (#(# Ðà0  àà ° àBriscoe,€T.,€&€Copestake,€A.€(1996).€Controlling€the€application€of€lexical€rules.€In€E.€Viegas€&ÏM.€Palmer€(Eds.),€òòBreadth€and€Depth€of€Semantic€Lexiconsóó.€Workshop€Sponsored€by€theÐ ''Æ $ ÐSpecial€Interest€Group€on€the€Lexicon.€Santa€Cruz,€CA:€Association€for€ComputationalÏLinguistics.Ð (#(# Ðà0  àà ° àBruce,€R.,€&€Guthrie,€L.€(1992).€Genus€disambiguation:€A€study€of€weighted€preference.Ïâ âCOLING92.ÐË*j$((#(# Ðà0  àà ° àBuitelaar,€P.€(1997).€A€lexicon€for€underspecified€semantic€tagging.€4th€Meeting€of€the€ACLÏâ âSpecial€Interest€Group€on€the€Lexicon.€Washington,€DC:€Association€for€ComputationalÏLinguistics.Ð (#(# Ðà0  àà ° àBurstein,€J.,€Kaplan,€R.,€Wolff,€S.,€&€Lu,€C.€(1996).€Using€lexical€semantic€informationÏtechniques€to€classify€free€responses.€In€E.€Viegas€&€M.€Palmer€(Eds.),€òòBreadth€andÐ  ¤ ÐDepth€of€Semantic€Lexiconsóó.€Workshop€Sponsored€by€the€Special€Interest€Group€on€theÐ î  ÐLexicon.€Santa€Cruz,€CA:€Association€for€Computational€Linguistics.Ð (#(# Ðà0  àà ° àBurstein,€J.,€Wolff,€S.,€Lu,€C.,€&€Kaplan,€R.€(1997).€An€automatic€scoring€system€for€AdvancedÏPlacement€biology€essays.€Fifth€Conference€on€Applied€Natural€Language€Processing.ÏWashington,€DC:€Association€for€Computational€Linguistics.Ð (#(# Ðà0  àà ° àCarlson,€L.,€&€Nirenburg,€S.€(1990).€òòWorld€Modeling€for€NLPóó€[CMU-CMT-90-121].€Pittsburgh,Ð {  ÐPA:€Carnegie€Mellon€University,€Center€for€Machine€Translation.Ð (#(# Ðà0  àà ° àChodorow,€M.,€Byrd,€R.,€&€Heidorn,€G.€(1985).€Extracting€semantic€hierarchies€from€a€largeÏon-line€dictionary.€23rd€Annual€Meeting€of€the€Association€for€ComputationalÏLinguistics.€Chicago,€IL:€Association€for€Computational€Linguistics.Ð (#(# Ðà0  àà ° àCopestake,€A.€(1990).€An€approach€to€building€the€hierarchical€element€of€a€lexical€knowledgeÏbase€from€a€machine-readable€dictionary.€First€International€Workshop€on€Inheritance€inÏNatural€Language€Processing.€Tilburg,€The€Netherlands.Ð (#(# Ðà0  àà ° 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