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1、AGraph-basedApproachforContextualTextNormalizationC¸agılS˘onmezandArzucan¨Ozg¨ur¨DepartmentofComputerEngineeringBogaziciUniversityBebek,34342Istanbul,Turkey{cagil.ulusahin,arzucan.ozgur}@boun.edu.trAbstractlolmeaninglaughingoutloud,xoxomeaningkiss-ing
2、,4umeaningforyouareamongthemostcom-Theinformalnatureofsocialmediatextmonlyusedexamplesofthisjargon.Inaddition,rendersitverydifficulttobeautomati-theseinformalexpressionsinsocialtextusuallycallyprocessedbynaturallanguagepro-takemanydifferentlexicalforms
3、whengeneratedcessingtools.Textnormalization,whichbydifferentindividuals(Eisenstein,2013).Thecorrespondstorestoringthenon-standardlimitedaccuraciesoftheSpeech-to-Text(STT)wordstotheircanonicalforms,providesatoolsinmobiledevices,whichareincreasinglybe-s
4、olutiontothischallenge.Weintroduceaningusedtopostmessagesonsocialmediaplat-unsupervisedtextnormalizationapproachforms,alongwiththescarcityofattentionofthatutilizesnotonlylexical,butalsocon-theusersresultinadditionaldivergenceofso-textualandgrammatical
5、featuresofsocialcialtextfrommorestandardtextsuchasfromtext.Thecontextualandgrammaticalfea-thenewswiredomain.Toolssuchasspellcheckerturesareextractedfromawordassociationandslangdictionarieshavebeenshowntobein-graphbuiltbyusingalargeunlabeledso-sufficien
6、ttocopewiththischallengelongtimecialmediatextcorpus.Thegraphencodesago(Sproatetal.,2001).Inaddition,mostNat-therelativepositionsofthewordswithre-uralLanguageProcessing(NLP)toolsincludingspecttoeachother,aswellastheirpart-of-namedentityrecognizersandde
7、pendencyparsersspeechtags.Thelexicalfeaturesareob-generallyperformpoorlyonsocialtext(Ritterettainedbyusingthelongestcommonsub-al.,2010).sequenceratioandeditdistancemeasurestoencodethesurfacesimilarityamongTextnormalizationisapreprocessingsteptowords,a
8、ndthedoublemetaphonealgo-restorenon-standardwordsintexttotheirorigi-rithmtorepresentthephoneticsimilarity.nal(canonical)formstomakeuseinNLPapplica-Unlikemostoftherecentapproachesthattionsormorebroadlytounderstandthedigitizedarebasedongeneratin