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1、SupportingAnnotationLayersforNaturalLanguageProcessingArchanaGanapathiandPreslavNakovandArielSchwartzandMartiHearstCSDivisionandSIMSUniversityofCalifornia,Berkeley archanag,nakov,sariel@cs.berkeley.edu,hearst@sims.berkeley.eduAbstractofdescription.Wedemonstratethepowerofthequery
2、languageandtheefficiencyoftheindexingThispaperproposesasimplemecha-architectureonawidevarietyofquerytypesthatnismforsupportingmultipleoverlap-havebeenpublishedintheNLPliterature.pinglayersofannotationsfornaturalThearchitectureisbuiltontopofarelationallanguagetextprocessing.Weprese
3、ntdatabasemanagementsystem(RDBMS),andsoaquerylanguageforflexiblyaccessingcantakeadvantageofadvancedindexingstruc-annotatedtext,anddemonstrateitsuseturessuppliedbysuchsystems.WebelievethisonexamplestakenfromtheNLPliter-paperisthefirsttoexperimentwithdifferentin-ature.Wethenreportone
4、xperimentsdexingstructuresinordertodeterminehowtocomparingdifferentstorageandindex-makeannotation-basedqueriesscaletoverylargeingarchitecturesusinganRDBMS,corporawithmanylayersofannotations.showingthatannotation-basedqueriesAnaddedadvantageofthisrepresentationiscanbemadetoscaleto
5、largecorporathatitsupportsaninfrastructureforrunningcom-withmanylayersofannotations.putationallinguisticsexperiments.OftenNLPal-gorithmsrequiresamplingfromadocumentcol-1Introductionlection,eithertoensurethattrainingandtestingareconductedoverrepresentativedata,ortofindTodaymostnatu
6、rallanguageprocessing(NLP)al-sufficientnumbersofsentencesthatsatisfycertaingorithmsmakeuseoftheresultsofpreviouspro-properties.Forexample,adisambiguationalgo-cessingsteps.Forexample,awordsensedisam-rithmmayneedtotrainonexamplesofawordbiguationalgorithmmayusetheoutputofato-withinva
7、riousdomains,sinceitsmeaningsmaykenizer,apart-of-speechtagger,aphrasebound-varyacrossdifferentsubcollections.aryrecognizer,andamodulethatclassifiesnounIntheremainderofthepaperwedescribere-phrasesintosemanticcategories.Currentlytherelatedwork,illustratetheannotationmodelandtheisnos
8、tandardwaytorepresentandstorethere-query