Feature Terms Analyzing Strategy for Recruiting Websites

Xu Hong, Yongjun Zhang, Zhang Jiong

Business Computing and Global Informatization(2012)

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摘要
As we know text information on web page has grown exponentially. It is a hot research area in data processing by reasonably extracting and analysis for unstructured information, so as to mine novel, latent useful pattern. Focusing on imprecise classified text set about job hunting web site, discovering topic relevant feature terms is an effective way to find new tendency for work ability demanding. In this paper, we propose a job relevant feature extracting method better than methods of TF-IDF, maximum entropy and lexical chain to reflect the demanding of tendency, and prove that it is effective by contrast testing.
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关键词
job hunting web site,maximum relevance,new tendency,web page,unstructured information analysis,contrast testing,imprecise classified text,concurrent terms,information retrieval,hot research area,unstructured information,data processing,feature terms,latent useful pattern,job relevant feature,classified text set,job relevant feature extracting method,text information,feature extraction,web sites,recruiting websites,classification,topic relevant feature term,text analysis,unstructured information extraction,feature terms analyzing strategy,feature term extraction,entropy,business,information technology
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