Toward Large-Scale Information Retrieval Using Latent Semant(14)

2020-12-24 20:52

I am deeply indebted to Dr. Michael Berry, my major advisor, for his kind guidance and support. I also thank Dr. Susan Dumais, director of the Information Sciences Research Group at Bellcore, for her technical advice. In addition, she graciously allowed us

processofcreatingpro lesandmatchingdocumentswiththepro lesissimilartorelevancefeedback,atechniqueusedbyinformationretrievalsystemstoenrichauser’squery.RelevancefeedbackwillbediscussedinSection2.2.6.

Sinceinformation lteringmustdealwithsomeofthesameissuesasinformationretrieval(forexample,differencesinwordusage),conceptualinformationretrievalmodelsareoftenusedforinformation lteringapplications.Sofar,resultshaveindi-catedthatinformation lteringisusefulfordecreasingtheamountofinformationapersonmustconsider,whilestillprovidingtheinformationheorshemightconsideruseful[FD92].

1.3TheDevelopmentofanLSIProgrammingLibrary

Thefollowingchaptersoutlinethedevelopmentanduseofanef cient,extensible,portable,andmaintainableimplementationoftheLatentSemanticIndexingmodel.Chapter2exploresthegeneralideasbehindvector-spacemodelsforinformationre-trievalanddescribesLSIinparticular.Chapter3introducesLSI++,aC++classlibraryforsearchingwiththeLSImodel,andChapter4examineshowLSI++canbeusedtobuildbothserialanddistributedsearchengines.Inaddition,Chapter4presentsaWorld-WideWebinterfaceforLSIthatprovidesuserstheopportunitytointeractwiththeLSI++searchengines.Finally,Chapter5examinestheexecutionef ciencyofthesearchengineswrittenwithLSI++.

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