Maximum Entropy Markov Models for Information Extraction and Segmentation

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1、MaximumEntropyMarkovModelsforInformationExtractionandSegmentationAndrewMcCallumMCCALLUM@JUSTRESEARCH.COMDayneFreitagDAYNE@JUSTRESEARCH.COMJustResearch,4616HenryStreet,Pittsburgh,PA15213USAFernandoPereiraPEREIRA@RESEARCH.ATT.COMAT&TLabs-Research,180ParkAve,FlorhamPark,N

2、J07932USAAbstractforstate-transitionprobabilitiesandstate-specificobserva-tionprobabilities.GreatlycontributingtotheirpopularityHiddenMarkovmodels(HMMs)areapowerfulistheavailabilityofstraightforwardproceduresfortrain-probabilistictoolformodelingsequentialdata,ingbymaxim

3、umlikelihood(Baum-Welch)andforusingandhavebeenappliedwithsuccesstomanythetrainedmodelstofindthemostlikelyhiddenstatese-text-relatedtasks,suchaspart-of-speechtagging,quencecorrespondingtoanobservationsequence(Viterbi).textsegmentationandinformationextraction.Inthesecases

4、,theobservationsareusuallymod-Intext-relatedtasks,theobservationprobabilitiesaretyp-eledasmultinomialdistributionsoveradiscreteicallyrepresentedasamultinomialdistributionoveradis-vocabulary,andtheHMMparametersaresetcrete,finitevocabularyofwords,andBaum-Welchtrainingtoma

5、ximizethelikelihoodoftheobservations.isusedtolearnparametersthatmaximizetheprobabilityofThispaperpresentsanewMarkoviansequencetheobservationsequencesinthetrainingdata.model,closelyrelatedtoHMMs,thatallowsob-Therearetwoproblemswiththistraditionalapproach.servationstober

6、epresentedasarbitraryoverlap-First,manytaskswouldbenefitfromaricherrepresenta-pingfeatures(suchasword,capitalization,for-tionofobservations—inparticulararepresentationthatde-matting,part-of-speech),anddefinesthecondi-scribesobservationsintermsofmanyoverlappingfeatures,ti

7、onalprobabilityofstatesequencesgivenob-suchascapitalization,wordendings,part-of-speech,for-servationsequences.Itdoesthisbyusingthematting,positiononthepage,andnodemembershipsinmaximumentropyframeworktofitasetofexpo-WordNet,inadditiontothetraditionalwordidentity.Fornenti

8、almodelsthatrepresenttheprobabilityofaexample,whentryingtoextractpreviouslyunseencom-stategivenanobservationandthepre