IEEE标准格式

TextRecognitionwithMachineLearning

basedonTextStructure

LiteratureReview

YifanShiStudentID:27291944Email:ys1n13@soton.ac.ukMScArti cialIntelligence

FacultyofPhysicalSciences&Eng,UniversityofSouthampton

Abstract—ThefastdevelopingMachineLearningalgorithmsintroducedtosemanticareanowadayshasbroughtvasttechniquesintextrecognition,classi cation,andprocessing.However,thereisalwaysacontradictionbetweenaccuracyandspeed,ashigheraccuracygenerallyrepresentsmorecomplicatedsystemaswellaslargetrainingdatabase.Inordertoachieveabalancebetweenfastspeedandgoodaccuracy,manybrilliantdesignsareusedintextprocessing.Inthisliteraturereview,theseeffortsareintroducedinthreelayers:Natural-LanguageProcessing,TextClassi cation,andIBMWatsonSystem.Keywords—MachineLearning,Natural-LanguageProcessing,TextClassi cation,IBMWatson

asitsworkingpipeline.Finally,aconclusionwillbeincludedtogivesomecommentsonthesetechniques.

II.NATURALLANGUAGEPROCESSINGInordertodealwiththehumannatural-language,itisnecessarytotransformtheunstructuredtextintowell-structuredtablesofexplicitsemantics(Ferrucci,2012).AccordingtoLiddy(2001),Natural-LanguageProcessing(NLP)isaseriesofcomputationaltechniquesusedtoanalyzeandrepresentnaturallyorganizedtextinordertoachievecertaintasksandapplications.CollobertandWeston(2008)havecategorizedNLPtasksintosixtypes:Part-Of-SpeechTagging,Chunking,NamedEntityRecognition,SemanticRoleLabeling,LanguageModels,andSemanticallyRelatedWords.Inadditiontothis,theyalsoimplementedMultitaskLearningwithDeepNeuralNetworkstobuildasuccessfuluni edarchitecturewhichavoidedtraditionallargeamountofempiricalhand-designedfeaturestotrainthesystembyusingbackpropagationtraining(Collobertetal.,2011).III.TEXTCLASSIFICATION

Oneofthesimplewaytorepresentanarticleforalearningalgorithmistousethenumberoftimesthatdistinctwordsappearinthedocument(Joachims,2005).However,duetothelargeamountofpossiblewordsusedinarticles,itwouldcreateaveryhighdimensionalspaceoffeatures.Joachims(1999)suggestsaTransductive1

I.INTRODUCTION

ThegrowingpopularityoftheInternethasbroughtincreasingnumberofusersonline,withavastamountofmessages,blogs,articles,etc.tobedealtwith.Thesetexts,knownasnatural-languagetexts,containpossibleusefulinformationbuttakealongtimeforhumantoread,understandanddealwith.Despitethepopularsearchenginetechnologynowadaysinhelpingusersto ndthesourceswithkeywords,semantictechniquesarealsoneededbymanycompaniestoimprovetheiruser-friendlyworkingenvironment.Inthisliteraturereview,Iwillintroduceseveralimportantsemantictechniques,startingfromthemostbasicNatural-LanguageProcessing,concentratinginthemeaningofwordsandsentences,followedbyTextClassi cationwhichisfocusedonparagraphsandarticles.Then,IwillintroducealandmarksystemnamedIBMWatson,whichhasDeepQA