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面向新闻领域的小型垂直搜索引擎.docx

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面向新闻领域的小型垂直搜索引擎 Title:DesignandDevelopmentofaSmall-ScaleVerticalSearchEnginefortheNewsDomain Abstract: Intheageoftheinternet,whereinformationisabundant,theneedforefficientandprecisesearchengineshasbecomeparamount.Thispaperaimstopresentthedesignanddevelopmentofasmall-scaleverticalsearchenginespecificallytailoredforthenewsdomain.Thepurposeofthissearchengineistoprovideuserswithanenhancednewssearchexperiencebyindexingandretrievingrelevantnewsarticlesfromvarioussourceswithoptimalaccuracyandefficiency. 1.Introduction: Withavastamountofnewsarticlesbeingpublishedeveryday,itcanbechallengingforuserstofindthemostrelevantandup-to-dateinformation.Traditionalsearchengines,whileeffectiveingeneralsearchqueries,oftenlacktheabilitytofilterandprioritizenewsresults.Asmall-scaleverticalsearchenginefocusedonthenewsdomaincanbridgethisgapbyleveragingspecifictechniquesandalgorithmstoofferusersamorerefinedandtargetedsearchexperience. 2.InformationRetrievalTechniques: Tobuildanefficientnewssearchengine,itisessentialtoutilizeappropriateinformationretrievaltechniques.Theprocessinvolvescrawlingandindexingnewsarticlesfromvarioussourcesandimplementingalgorithmsforretrievalandranking.Commontechniquesincludewebcrawling,textpreprocessing,termfrequency-inversedocumentfrequency(TF-IDF),andPageRank. 2.1WebCrawling: Webcrawlinginvolvesthesystematicandautomatedexplorationofwebpagestoextractcontent.Foranewssearchengine,afocusedcrawlercanbeemployedtotargetnewswebsitesandgatherrelevantnewsarticles.Thisensuresthatthesearchengineisconstantlyupdatedwiththelatestnews. 2.2TextPreprocessing: Textpreprocessingtechniquessuchastokenization,stop-wordremoval,stemming,andnamedentityrecognitioncanimprovethequalityandefficiencyofsearchresults.Byremovingunnecessaryelements,normalizingwords,andrecognizingrelevantentities,thesearchenginecanbetterunderstandandcategorizenewsarticles. 2.3TF-IDF: Termfrequency-inversedocumentfrequency(TF-IDF)isapopulartechniqueusedtodeterminetheimportanceofatermwithinadocument.Bycalculatingthefr