ÿWPCô w‚x¥=Žˆ(³A>æ~ÔY.ò#»qL†@¶-Jó·J ;GŽÊíR«Ù`®íº$xÖ&Úä3ü©’îÿz‣Ä`¸BP{+Pe*Ì•üGJ=Thµ 6ŸŒÙÿäÖÝÒŽô®;˜1än—ÃåœtÄ ¼KK¿*½ïW“ùU󨀤QPíèƒñcŒ.É•Ž'vàegnSwþš5a†ÕOió¤V™,GJ¼ “ ´ eHæ?Aðƒ”SÄ/üÓ(Æýl;XÀlŽ †{o÷À¦U—ÔÕžS´M%2:ßÀž )Ï–ëôÞÆƒËÙßèD…=6tr„§Èši&ÂHAÙ(Ŧ¸¦£S]h%Ûi4aCš$åN­EïV ”z¥AÝê¹í´¾ÑPé/¢£­¿9™ñbm›ko OÀÑbÏ6¢/­F¸.»ˆî'‹h§j›8à}š<Œ"nHj¤lº9fdvo úhá; -Ýv5Ú-+Çtõp°$Zo?T‚Ÿ£=ïšP”¤Œ_É_’Ñ/‘ÍE ¢oøxsµ¸Î(¨|aq*oŽ©¥ªm†5rF©Þ-V"°¸}Ì*/E¶èLÖc„Æ»ð}<¼p˜Æ/Šï{a |†Ò‘ÚŽ´/ж£€tŠH_B%#†U NŒ %Ú 0(à8UN@ÇŽ4Ui 0 ‹x 0 D cG ª ó«  ž .> > úll 0Df 0Dªéîîîîîîîîîîîîîî B×HP LaserJet IIIPHP3P.PRS,\,,\,ð*ØP O8gOº/î#O(@øÐ Z ‹6Times New Roman RegularX($¡¡Desiderata for Tagging with WordNet and MCCA (w/refs)Í % '  ðÖÃ9 Z‹6Times New Roman Regular ½Ý ƒ ­ $ÝÓ  ÓòòÚ  Ú1Ú  ÚóóÔ€òƒIòXXÔÝ  ÝA€word€may€have€more€than€one€category€and€isÐ ° Ðdisambiguated€in€tagging.$·J"!ìÃÿÿ3|x’(+82­ $¤¤Ý ƒ!ÝÝ  ÝÓ  ÓòòÚ  Ú0Ú  ÚóóÔ€òƒIòXXÔ(#Ã$òòÚ  Ú0Ú  Úóó YÝ ƒ ­ $ÝÓ  ÓòòÚ  Ú2Ú  ÚóóÔ€òƒIòXXÔÝ  ÝDisambiguation€is€based€on€a€running€context€score.€Ð ° ÐEach€category€has€a€frequency€of€occurrence€in€aÏcontext.€€The€category€selected€for€an€ambiguousÏword€is€the€one€with€the€smallest€difference€from€theÏrunning€context€score. ÷Ý ƒ ­ $ÝÓ  ÓòòÚ  Ú3Ú  ÚóóÔ€òƒIòXXÔÝ  ÝThis€dictionary€has€tagged€85€to€95€percent€of€theÐ ° Ðwords€in€about€1500€analyses€covering€45€millionÏwords€over€the€last€15€years. éÝ ƒ ­ $ÝÓ  ÓòòÚ  Ú5Ú  ÚóóÔ€òƒIòXXÔÝ  ÝIn€general,€we€have€found€that€assignment€of€onlyÐ ° Ðabout€5€to€10€percent€of€the€words€in€a€category€isÏquestionable. –Ý ƒ ­ $ÝÓ  ÓòòÚ  Ú6Ú  ÚóóÔ€òƒIòXXÔÝ  ÝAnalysis€of€MCCA€categories€is€a€continuing€process. $Ý ƒ ­ $ÝÓ  ÓòòÚ  Ú8Ú  ÚóóÔ€òƒIòXXÔÝ  ÝAgglomerative€techniques€cluster€the€two€closestÐ ° Ðtexts€(with€whatever€distance€metric)€and€thenÏsuccessively€add€texts€one„by„one€as€they€are€closestÏto€the€existing€cluster. ðÝ ƒ ­ $ÝÓ  ÓòòÚ  Ú7Ú  ÚóóÔ€òƒIòXXÔÝ  ÝIdentification€of€these€synsets€facilitates€extension€ofÐ ° Ðthe€MCCA€dictionary€to€include€further€hyponyms€ofÏthese€synsets.(3¯$§§Ý ƒ!ÝÝ  Ý(3¯$©©Ý ƒ!ÝÝ  Ý ßÝ ƒ ­ $ÝÓ  ÓòòÚ  Ú4Ú  ÚóóÔ€òƒIòXXÔÝ  ÝA€suite€of€programs€for€creating€and€maintainingÐ ° Ðlexicons€for€natural€language€processing,€availableÏfrom€CL€Research.€€Procedures€used€in€this€paper,Ïapplicable€to€any€category€analysis€using€DIMAP,€areÏavailable€at€https://www.clres.com.€€The€generalÏprinciples€of€category€development€followed€in€theseÏprocedures€are€described€in€(Litkowski,€in€preparation). dÝ ƒ!ÝÝ  ÝÑ  Ñà@’’ ìàò òDesiderata€for€Tagging€with€WordNet€Synsets€or€MCCA€Categoriesó óˆÐ ° ÐÌà@vv ìàKenneth€C.€LitkowskiˆÌà@åå%ìàCL€ResearchˆÌà@kk ìà20239€Lea€Pond€PlaceˆÌà@ûûìàGaithersburg,€MD€20879ˆÌà@ìà(Email:€ken@clres.com)ˆÌà@²²ìà(Web€site:€https://www.clres.com)ˆÌÌÔ€òƒIòXXÔÑ7€|¾XXdédÈ7ÑÒ(X°(#°(#(ÒÓ  ÓÓo[°X°Ü` ¸ hÀDÈ xÐ (#€%Ø'0*ˆ,à.813è5@8˜:ð<H? A°œXoÓò òòò1€€Abstractó óóóÐ â 2  ÐÌññà8 Ü àññMinnesota€Contextual€Content€AnalysisÏ(MCCA)€is€a€technique€for€characterizing€theÏconcepts€and€themes€occurring€in€textÏ(sentences,€paragraphs,€interview€transcripts,Ïbooks).€€MCCA€tags€each€word€with€aÏcategory€and€examines€the€distribution€ofÏcategories€against€norms€representing€generalÏusage€of€categories.€€MCCA€also€scores€textsÏin€terms€of€social€contexts€that€are€similar€toÏdifferent€functions€of€language.€€DistributionsÏcan€be€analyzed€using€non„agglomerativeÏclustering€to€characterize€the€concepts€andÏthemes.€€MCCA€categories€have€been€mappedÏto€WordNet€senses.€€The€definingÏcharacteristics€that€emerge€from€the€mappingÏand€the€statistical€techniques€used€in€MCCAÏfor€analyzing€concepts€and€themes€suggestÏthat€tagging€with€WordNet€synsets€or€MCCAÏcategories€may€produce€epiphenomenal€resultsÏthat€are€misleading.€€We€suggest€that€WordNetÏsynsets€and€MCCA€categories€be€augmentedÏwith€further€lexical€semantic€information€forÏuse€after€text€is€tagged€or€categorized.€€WeÏsuggest€that€such€information€is€useful€notÏonly€for€the€primary€purposes€ofÏdisambiguation€in€parsing€and€textÏclassification€in€content€analysis€andÏinformation€retrieval,€but€also€for€tasks€inÏcorpus€analysis,€discourse€analysis,€andÏautomatic€text€summarization.Рܔܔ ÐÌÑ7€|¾XXdéXXdé7Ñò òòò2€€Introductionó óóóÐ ë%;!* ÐÌContent€analysis€provides€distributional€methods€forÏanalyzing€characteristics€of€textual€material.€€Its€rootsÏare€the€same€as€computational€linguistics€(CL),€but€itÏhas€been€largely€ignored€in€CL€until€recently€(Dunning,Ï1993;€Carletta,€1996;€Kilgarriff,€1996).€€One€contentÏanalysis€approach,€Minnesota€Contextual€ContentÏAnalysis€(MCCA)€(McTavish€&€Pirro,€1990),€in€use€forÏover€20€years€and€with€a€well„developed€dictionaryÏñ‘ññ‘ñcategory€system,€contains€analysis€methods€that€provideÐ -í(4 Ðinsights€into€the€use€of€WordNet€(Miller,€et€al.,€1990)Ïfor€tagging.ÌÌWe€describe€the€unique€characteristics€of€MCCA,€howÏits€categories€relate€to€WordNet€synsets,€the€analysisÏmethods€used€in€MCCA€to€provide€quantitativeÏinformation€about€texts,€what€implications€this€has€forÏthe€use€of€WordNet€in€tagging,€and€how€theseÏtechniques€may€contribute€to€lexical€semantic€tagging.€ÏSpecifically,€we€show€that€WordNet€provides€aÏbackbone,€but€that€additional€lexical€semanticÏinformation€needs€to€be€associated€with€WordNetÏsynsets.€€We€describe€novel€perspectives€on€how€thisÏinformation€can€be€used€in€various€NLP€tasks.ÌÌÔ  Ôò òòò3€€Minnesota€Contextual€Content€Analysisóóó óÐ 1C ÐÌMCCA€differs€from€other€content€analysis€techniquesÏin€using€a€norm€for€examining€the€distribution€of€itsÏcategories€in€a€given€text.€€The€116€categories€used€inÏthe€dictionary€to€characterize€words,׃×Ý ƒ #ÃÝòòÚ  Ú1Ú  ÚóóÝ  Ý×  ×€like€other€contentÐ nH Ðanalysis€category€systems,€are€heuristic€in€nature.€€EachÏcategory€has€a€name€(e.g.,€òòactivity,€fellow€feeling,€aboutÐ  ðJ Ðchanging,€human€roles,€expression€arenaóó).Ð a±K ÐÌThe€distinguishing€characteristic€of€MCCA€is€that€theÏemphasis€of€each€category€is€normed€in€two€ways.€ÏCategories€that€are€emphasized€in€a€text€(E-scores)€areÏnormed€against€expected€general€usage€of€categoriesÏbased€on€the€Brown€corpus€(Kucera€&€Francis,€1967).€ÏThe€second€way€is€based€on€relative€usage€of€categoriesÏexpected€in€four€broad€institutional€areas.€€The€latter€isÏbased€on€some€initial€research€and€subsequent€workÏwhich€essentially€factor„analyzed€profiles€of€categoryÏusage€for€texts€representing€a€broad€range€ofÏorganizations€and€social€situations€(Cleveland,€et€al.,Ï1974).€€These€are€referred€to€as€context€scoresÏ(C-scores)€and€labelled€òòtraditionalóó€(judicial€andÐ ï(?$Y Ðreligious€texts),€òòpracticalóó€(business€texts),€òòemotionalóóÐ °)%Z Ð(leisure,€recreational,€and€fictional€texts),€€and€òòanalyticóóÐ q*Á%[ Ð(scientific€writings).€€These€contexts€correspond€well€toÏthe€functions€of€language€(Nida,€1975:€201-5).ÌÌAfter€tagging€a€text€and€determining€categoryÏfrequencies,€the€C-scores€are€calculated€by€comparisonÏwith€the€expected€distribution€of€the€contexts€and€theÏE-scores€are€calculated€by€comparison€with€theÏexpected€distribution€of€each€category.׃ ×Ý ƒ #ÃÝòòÚ  Ú2Ú  ÚóóÝ  Ý×  ×€€These€are€theÐ ÷ G Ðquantitative€bases€for€analysis€of€the€concepts€andÏthemes.ÌÌ€Unlike€other€techniques€for€determining€which€wordsÏare€characteristic€of€a€text€(Kilgarriff,€1996),€such€as€theÏð/ðòò2óó„test€and€mutual€information,€the€C-scores€andÐ }Í  ÐE-scores€are€examined€not€only€for€differences€amongÏtexts,€but€also€for€over„€and€under„emphasis€against€theÏnorms.€€This€provides€greater€sensitivity€to€the€analysisÏof€concepts€and€themes.ÌÌÔ  Ôò òòò4€€MCCA€Categories€andWordNet€Synsetsóóó óÐ S ÐÌ€(McTavish,€et€al.,€1995)€and€(McTavish,€et€al.,€1997)Ïsuggest€that€MCCA€categories€recapitulate€WordNetÏsynsets.€€We€used€WordNet€synsets€in€examiningÏMCCA€categories€to€determine€their€coherence,€toÏcharacterize€their€relations€with€WordNet,€and€toÏunderstand€the€significance€of€these€relations€in€theÏMCCA€analysis€of€concepts€and€themes€and€in€taggingÏwith€WordNet€synsets.ÌÌIn€the€MCCA€dictionary€of€11,000€words,׃ U×Ý ƒ #ÃÝòòÚ  Ú3Ú  ÚóóÝ  Ý×  ×€the€averageÐ vÆ Ðnumber€of€words€in€a€category€is€95,€with€a€range€fromÏ1€to€about€300.€€Using€the€DIMAP€software€(CLÏResearch,€1997€-€in€preparation),׃`×Ý ƒ #ÃÝòòÚ  Ú4Ú  ÚóóÝ  Ý×  ×€we€createdÐ ¹ ! Ðsublexicons€of€individual€categories,€extractedÏWordNet€synsets€for€these€sublexicons,€extractedÐ ;‹# Ðinformation€from€the€Merriam„Webster€ConciseÏElectronic€Dictionary€integrated€with€DIMAP,€andÏattached€lexical€semantic€information€from€otherÏresources€to€entries€in€these€sublexicons.ÌÌWe€began€with€the€hypothesis€that€the€categoriesÏcorrespond€to€those€developed€by€(Hearst€&€SchðGðtze,Ï1996)€in€creating€categories€from€the€WordNet€nounÏhierarchy.€€We€found€that€the€MCCA€categories€wereÏgenerally€internally€consistent,€but€with€characteristicsÏnot€intuitively€obvious.׃×Ý ƒ #ÃÝòòÚ  Ú5Ú  ÚóóÝ  Ý×  ×€€As€a€result,€we€needed€toÐ : Š- Ðarticulate€firm€principles€for€characterizing€theÏcategories.ÌÌEleven€categories€(such€as€òòHave,€Prepositions,€You,€I„Ð >Ž 1 ÐMe,€He,€A„An,€Theóó)€consist€of€only€a€few€words€fromÐ ÿO 2 Ðclosed€classes.€€The€category€òòTheóó€contains€one€wordÐ À 3 Ðwith€an€average€expected€frequency€of€6€percent€(withÏa€range€over€the€four€contexts€of€5.5€to€6.5).€€TheÏcategory€òòPrepositionsóó€contains€18€words€with€anÐ S6 Ðaverage€expected€frequency€of€11.1€percent€(with€aÏrange€over€the€four€contexts€of€9.5€to€12.3€percent).€ÏAbout€20€categories€(òòImplication,€If,€Colors,€Object,Ð F–9 ÐBeingóó)€consist€of€a€relatively€small€number€of€wordsÐ W: Ð(34,€22,€65,€11,€12,€respectively)€taken€primarily€fromÏsyntactically€or€semantically€closed„class€wordsÏ(subordinating€conjunctions,€relativizers,€the€tops€ofÏWordNet,€colors).ÌÌThe€remaining€80€or€so€categories€consist€primarily€ofÏopen„class€words€(nouns,€verbs,€adjectives,€andÏadverbs),€sprinkled€with€closed„class€words€(auxiliaries,Ïsubordinating€conjunctions).€€These€categories€requireÏmore€detailed€analyses.׃Ó×Ý ƒ #ÃÝòòÚ  Ú6Ú  ÚóóÝ  Ý×  ×Ð ‘áD ÐÌSeveral€categories€correspond€well€to€the€Hearst€&ÏSchtze€model.€€The€categories€òòFunctional€roles,Ð Ô$G ÐDetached€roles,€óóand€òòHuman€rolesóó€align€with€subtreesÐ • åH Ðrooted€at€particular€nodes€in€the€WordNet€hierarchies.€ÏFor€example,€òòDetached€rolesóó€has€a€total€of€66€words,Ð "gJ Ðwith€an€average€expected€frequency€of€.16€percent€andÏa€range€from€.10€to€.35€percent.€€The€.35€percentÏfrequency€is€for€the€òòanalyticóó€context;€each€of€the€otherÐ Z$ªM Ðthree€contexts€have€expected€frequencies€of€about€.10Ïpercent.€€The€words€in€this€category€include:ÌÌà0 D àòòÔ‡òƒIòŽòƒIÔacademic,€artist,€biologist,€creator,€critic,Ð ^'®"Q Ðhistorian,€instructor,€observer,€philosopher,Ð (o#RD(#D(# Ðphysicist,€professor,€researcher,€reviewer,Ïscientist,€sociologistÔ#†òƒIòŽòƒI¨##ÔóóÐqÁÜÀÜÀ ÐÌThese€words€are€a€subset€of€the€WordNet€synsetsÏheaded€at€òòpersonóó,€in€particular,€synsets€headed€byÐ ´ ÐÌòòcreatoróó;Ð 6 † Ðòòexpert:€authority:€professionalóó;Ð ÷ G Ðòòintellectualóó.׃×Ý ƒ #ÃÝòòÚ  Ú7Ú  ÚóóÝ  Ý×  ×Ð ¸  ÐÌOther€synsets€under€òòexpertóó€and€òòauthorityóó€do€not€fallÐ : Š  Ðinto€this€category.€€Thus,€the€heuristic€òòDetached€rolesóóÐ û K  Ðis€like€a€Hearst€&€Schtze€super„category,€but€notÏconstructed€on€a€statistical€metric,€rather€on€underlyingÏsemantic€components.ÌÌOther€categories€do€not€fall€out€so€neatly.€€The€categoryÏòòSanctionóó€(120€words)€has€an€average€expectedÐ Ñ  Ðfrequency€of€.08€percent,€with€a€range€over€the€fourÏcontexts€of€.06€to€.10€percent.€€It€includes€the€followingÏwords€(and€their€inflected€forms):ÌÌà0 Ü àòòÔ‡òƒIòŽòƒIÔapplaud,€applause,€approve,€congratulate,Ð F– Ðcongratulation,€convict,€conviction,Ïdisapproval,€disapprove,€honor,€judge,Ïjudgment,€judgmental,€merit,€mistreat,Ïreject,€rejection,€ridicule,€sanction,€scorn,Ïscornful,€shame,€shamefullyÔ#†òƒIòŽòƒI„(#ÔóóÐ [ÜÀÜÀ ÐÌExamination€of€the€WordNet€synsets€is€similarlyÏsuccessful€here,€identifying€many€words€(particularlyÏverbs)€in€a€subtree€rooted€at€òòjudgeóó.€€However,€the€setÐ _ Ðis€defined€as€well€by€including€a€derivational€lexicalÏrule€to€allow€forms€in€other€parts€of€speech.€€AnotherÏmeaning€component€is€seen€in€òòapproveóó€andÐ R¢" Ðòòdisapproveóó,€namely,€the€negative€or€pejorative€prefix,Ð c# Ðagain€requiring€a€lexical€rule€as€part€of€the€category'sÏdefinition.€€Such€lexical€rules€would€be€encoded€asÏdescribed€in€(Copestake€&€Briscoe,€1991).€This€set€ofÏwords€(rooted€primarily€in€the€verbs€of€the€set)Ïcorresponds€to€the€(Levin,€1993)€òòCharacterizeóó€(classÐ Ø"(( Ð29.2),€òòDeclareóó€(29.4),€òòAdmireóó€(31.2),€and€JudgmentÐ ™#é) Ðverbs€(33)€and€hence€may€have€particular€syntactic€andÏsemantic€patterning.€€The€verb€frames€attached€toÏWordNet€verb€synsets€are€not€sufficiently€detailed€toÏcover€the€granularity€necessary€to€characterize€anÏMCCA€category.€€Instead,€the€definition€of€this€classÏmight,€following€(Davis,€1996),€inherit€a€sort€òònotion„Ð (o#/ Ðrelóó,€which€has€a€"perceiver"€and€a€"perceived"Ð à(0$0 Ðargument€(thus€capturing€syntactic€patterning)€withÐ ¡)ñ$1 Ðperhaps€a€selectional€restriction€on€the€"perceiver"€thatÏthe€type€of€action€is€an€evaluative€one€(thus€providingÏsemantic€patterning).ÌÌAnother€complex€category€is€òòNormativeóó,€consisting€ofÐ ´5 Ð76€words,€with€an€average€expected€frequency€of€.60Ïpercent€and€a€range€over€the€four€contexts€of€.37€to€.79Ïpercent.€€This€category€also€has€words€from€all€parts€ofÏspeech€and€thus€will€entail€the€use€of€derivationalÏlexical€rules€in€its€definition.€€This€category€includesÏthe€following€(along€with€various€inflectional€forms):ÌÌà0 D àòòabsolute,€absolutely,€consequence,Ð ¼ = Ðconsequently,€correct,€correctly,Ïdogmatism,€habitual,€habitually,Ïideologically,€ideology,€necessarily,Ïnecessary,€norm,€obviously,€prominent,Ïprominently,€regularity,€regularly,Ïunequivocally,€unusual,€unusuallyóóÐB’ CD(#D(# ÐÌThe€use€of€the€heuristic€òòNormativeóó€to€label€this€categoryÐ ÄE Ðclearly€reflects€the€presence€in€these€words€of€aÏsemantic€component€oriented€around€characterizingÏsomething€in€terms€of€expectations.€€But,€of€particularÏinterest€here,€are€the€adverb€forms.€€McTavish€has€alsoÏused€the€heuristic€òòReasoningóó€for€this€category.€€TheseÐ ‰ÙJ Ðadverbs€are€òòcontent€disjunctsóó€(Quirk,€et€al.,€1985:Ð JšK Ð8.127-33),€that€is,€words€betokening€a€speaker'sÏcomment€on€the€content€of€what€the€speaker€is€saying,Ïin€this€case,€compared€to€some€norm€or€standard.€€Thus,Ïpart€of€the€defining€characteristics€for€this€category€is€aÏspecification€for€lexical€items€that€have€a€[content„¼disjunct€+]€feature.ÌÌThese€examples€of€words€in€the€òòSanctionóó€andÐ R¢S ÐòòNormativeóó€categories€(repeated€in€other€categories)Ð cT Ðindicates€a€need€to€define€categories€not€only€in€termsÏof€supercategories€using€the€Hearst€&€Schtze€model,Ïbut€also€with€additional€lexical€semantic€informationÏnot€present€in€WordNet€or€MCCA€categories.€€InÏparticular,€we€see€the€need€for€encoding€derivationalÏand€morphological€relations,€finer„grainedÏcharacterization€of€government€patterns,€featureÏspecifications,€and€primitive€semantic€components.ÌÌIn€any€event,€we€have€seen€that€MCCA€categories€areÏconsistent€with€WordNet€synsets.€€They€recapitulate€theÏWordNet€synsets€by€acting€as€supercategories€similar€toÏthose€identified€in€Hearst€&€Schtze.€€To€this€extent,Ïresults€from€MCCA€tagging€would€be€similar€to€thoseÏof€Hearst€&€Schtze.€€The€MCCA€methods€suggestÏfurther€insights€based€on€what€purposes€we€are€trying€toÏachieve€from€tagging.Ìâ âÐ ¥,õ'f Ðñ’ñÔ  Ôâ âñ’ñò òòò5€€Analysis€of€Tagged€Textsóóó óÐ ° ÐÌThe€important€questions€at€this€point€are€why€there€isÏvalue€in€having€additional€lexical€semantic€informationÏassociated€with€tagging€and€why€MCCA€categories€andÏWordNet€synsets€are€insufficient.€€The€answer€to€theseÏquestions€begins€to€emerge€by€considering€the€furtherÏanalysis€performed€after€a€text€has€been€"classified"€onÏthe€basis€of€the€MCCA€tagging.€€As€described€above,ÏMCCA€produces€a€set€of€C-scores€and€E-scores€forÏeach€text.€€These€scores€are€then€subjected€to€analysisÏto€provide€additional€results€useful€in€social€science€andÏinformation€retrieval€applications.ÌÌThe€two€sets€of€scores€are€used€for€computing€theÏdistance€among€texts.€€This€distance€is€used€directly€orÏin€exploration€of€the€differences€between€texts.€€UnlikeÏother€content€analysis€techniques€(or€classificationÏtechniques€used€for€measuring€the€distance€betweenÏdocuments€in€information€retrieval),€MCCA€uses€theÏnon„agglomerative€technique€of€multidimensionalÏscaling€(MDS).׃×Ý ƒ #ÃÝòòÚ  Ú8Ú  ÚóóÝ  Ý×  ×€€This€technique€(Kruskal€&€Wish,Ð ­ý Ð1977)€produces€a€map€when€given€a€matrix€ofÏdistances.ÌÌMDS€does€not€presume€that€a€2„dimensionalÏrepresentation€displays€the€distances€between€texts.€ÏRather,€it€unfolds€the€dimensions€one„by„one,€startingÏwith€2,€examines€statistically€how€"stressed"€theÏsolution€is,€and€then€adds€further€dimensions€until€theÏstress€shows€signs€of€reaching€an€asymptote.€€OutputÏfrom€the€scaling€provides€"rotation"€maps€at€eachÏdimension€projected€onto€2„dimensional€space.ÌÌMcTavish,€et€al.€illustrates€the€simple€and€the€moreÏcomplex€use€of€these€distance€metrics.€€In€the€simpleÏuse,€the€distance€between€transcripts€of€nursing€homeÏpatients,€staff,€and€administrators€was€used€as€aÏmeasure€of€social€distance€among€these€three€groups.€ÏThis€measure€was€combined€with€variousÏcharacteristics€of€nursing€homes€(size,€type,€location,Ïetc.)€for€further€analysis,€using€standard€statisticalÏtechniques€such€as€correlation€and€discriminantÏanalysis.ÌÌIn€the€more€complex€use,€the€MDS€results€identify€theÏconcepts€and€themes€that€are€different€and€similar€in€theÏtranscripts.€€This€is€accomplished€by€visually€inspectingÏthe€MDS€graphical€output.€€Examination€of€the€4„Ð )X$0 Ðñ’ñ¼ñ’ñdimensional€context€vectors€provides€an€initialÏcharacterization€of€the€texts.€€The€analyst€identifies€theÏcontextual€focus€(òòtraditional,€practical,€emotional,€óóorÐ 2‚2 Ðòòanalyticóó)€and€the€ways€in€which€the€texts€differ€fromÐ óC3 Ðone€another.€€This€provides€general€themes€and€pointersÏfor€identifying€the€conceptual€differences€among€theÏtexts.ÌÌMDS€analysis€of€the€E-score€vectors€identifies€theÏmajor€concepts€that€differentiate€the€texts.€€The€analystÏexamines€the€graphical€output€to€label€points€with€theÏdominant€MCCA€categories.€€The€"meaning"€(that€is,Ïthe€underlying€concepts)€of€the€MDS€graph€is€thenÏdescribed€in€terms€of€category€and€word€emphases.€ÏThese€are€the€results€an€investigator€uses€in€reportingÏon€the€content€analysis€using€MCCA.ÌÌThis€is€the€point€at€which€the€insufficiency€of€MCCAÏcategories€(and€WordNet€synsets)€becomes€visible.€€InÏexamining€the€MDS€output,€the€analysis€is€subjectiveÏand€based€only€on€identification€of€particular€sets€ofÏwords€that€distinguish€the€concepts€in€each€text€(muchÏlike€the€techniques€described€in€(Kilgarriff,€1996)€thatÏare€used€in€authorship€attribution).€€If€the€MCCAÏcategories€had€richer€definitions€based€on€additionalÏlexical€semantic€information,€the€analysis€could€beÏperformed€based€on€less€subjective€and€more€rigorouslyÏdefined€principles.ÌÌ€(Burstein,€et€al.,€1996)€describe€techniques€for€usingÏlexical€semantics€to€classify€responses€to€test€questions.€ÏAn€essential€component€of€this€classification€process€isÏthe€identification€of€sublexicons€that€cut€across€parts€ofÏspeech,€along€with€concept€grammars€based€onÏcollapsing€phrasal€and€constituent€nodes€into€aÏgeneralized€XP€representation.€€As€seen€above€in€theÏprocedures€for€defining€MCCA€categories,€addition€ofÏlexical€semantic€information€in€the€form€of€derivationalÏand€morphological€relations€and€semantic€componentsÏcommon€across€part€of€speech€boundariesð"ðinformationÏnow€lacking€in€WordNet€synsetsð"ðwould€facilitate€theÏdevelopment€of€concept€grammars.ÌÌ€(Briscoe€&€Carroll,€1997)€describe€novel€techniquesÏfor€constructing€a€subcategorization€dictionary€fromÏanalysis€of€corpora.€€They€note€that€their€system€needsÏfurther€refinement,€suggesting€that€adding€informationÏto€lexical€entries€about€diathesis€alternation€possibilitiesÏand€semantic€selectional€preferences€on€argument€headsÏis€likely€to€improve€their€results.€€Again,€the€proceduresÏfor€analyzing€MCCA€categories€seem€to€require€thisÏtype€of€information.ÌÌWe€have€discussed€elsewhere€(Litkowski€&€Harris,Ï1997)€extension€of€a€discourse€analysis€algorithmÐ f-¶(f Ðincorporating€lexical€cohesion€principles.€€In€thisÏextension,€we€found€it€necessary€to€require€use€of€theÏòòagentiveóó€and€òòconstitutiveóó€qualia€of€nouns€(seeÐ 2‚ Ð(Pustejovsky,€1995:€76))€as€selectional€specifications€onÏverbs€to€maintain€lexical€cohesion.€€With€suchÏinformation,€we€were€able€not€only€to€provide€a€moreÏcoherent€discourse€analysis€of€a€text€segment,€but€alsoÏpossibly€to€summarize€the€text€better.ÌÌò òòò6€€Discussion€and€Future€Workóóó óÐ y É  ÐÌWe€have€shown€how€MCCA€categories€generallyÏrecapitulate€WordNet€synsets€and€how€MCCA€analysisÏleads€to€thematic€and€conceptual€characterization€ofÏtexts.€€Since€MCCA€categories€do€not€exactlyÏcorrespond€to€WordNet€subtrees,€but€frequentlyÏrepresent€a€bundle€of€syntactic€and€semantic€properties,Ïwe€believe€that€the€tagging€results€are€epiphenomenal.€ÏSince€the€MCCA€results€seem€more€robust€than€taggingÏwith€WordNet€synsets€(q.v.€(Voorhees,€1994)),€weÏsuggest€that€this€is€due€to€more€specific€meaningÏcomponents€underlying€the€MCCA€categories.ÌÌ€(Nida,€1975:€174)€characterized€a€semantic€domain€asÏconsisting€of€words€sharing€semantic€components.€ÏHowever,€he€also€suggests€(Nida,€1975:€193)€thatÏdomains€represent€an€arbitrary€grouping€of€theÏunderlying€semantic€features.€€We€suggest€that€theÏMCCA€categories€and€WordNet€synsets€represent€twoÏsuch€systems€of€domains,€each€reflecting€particularÏperspectives.ÌÌThis€suggests€that€categorical€systems€used€for€taggingÏneed€to€be€augmented€with€more€precise€lexicalÏsemantic€information.€€This€information€can€beÏsemantic€features,€semantic€roles,€subcategorizationÏpatterns,€syntactic€alternations€(e.g.,€see€(Dorr,€inÏpress)),€and€semantic€components.€€We€suggest€that€theÏuse€of€this€lexical€semantic€information€in€tagging€mayÏprovide€considerable€benefit€in€analyzing€taggingÏresults.ÌÌWe€are€continuing€analysis€of€the€MCCA€categories€toÏcharacterize€them€in€terms€of€lexical€semanticÏinformation.€€We€are€using€a€variety€of€lexicalÏresources,€including€WordNet,€the€database€by€(Dorr,Ïin€press)€based€on€(Levin,€1993),€and€COMLEXÏ(Macleod€&€Grishman,€1994;€Wolff,€et€al.,€1995).€€WeÏwill€propagate€these€meaning€components€to€the€lexicalÏitems.ÌÌAfter€automating€the€MDS€analysis,€we€will€examineÏthe€extent€to€which€the€lexical€semantic€information€isÏâ 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