A Novel Approach for Ontology-Based Feature Vector Generation for Web Text Document Classification
نویسندگان
چکیده
Thetaskofextractingtheusedfeaturevectorinminingtasks(classification,clustering...etc.)is consideredthemostimportanttaskforenhancingthetextprocessingcapabilities.Thispaperproposes anovelapproachtobeusedinbuildingthefeaturevectorusedinwebtextdocumentclassification process;addingsemanticsinthegeneratedfeaturevector.Thisapproachisbasedonutilizingthe benefitofthehierarchalstructureoftheWordNetontology,toeliminatemeaninglesswordsfromthe generatedfeaturevectorthathasnosemanticrelationwithanyofWordNetlexicalcategories;this leadstothereductionofthefeaturevectorsizewithoutlosinginformationonthetext,alsoenriching the featurevectorbyconcatenatingeachwordwith its correspondingWordNet lexical category. Forminingtasks,theVectorSpaceModel(VSM)isusedtorepresenttextdocumentsandtheTerm FrequencyInverseDocumentFrequency(TFIDF)isusedasatermweightingtechnique.Theproposed ontologybasedapproachwasevaluatedagainstthePrincipalcomponentanalysis(PCA)approach, andagainstanontologybasedreductiontechniquewithouttheprocessofaddingsemanticstothe generatedfeaturevectorusingseveralexperimentswithfivedifferentclassifiers(SVM,JRIP,J48, Naive-Bayes,andkNN).Theexperimentalresultsrevealtheeffectivenessoftheauthors’proposed approachagainstothertraditionalapproachestoachieveabetterclassificationaccuracyF-measure, precision,andrecall.
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ورودعنوان ژورنال:
- IJSI
دوره 6 شماره
صفحات -
تاریخ انتشار 2018