DocumentCode :
2539102
Title :
Feature Terms Analyzing Strategy for Recruiting Websites
Author :
Hong, Xu ; Zhang, YongJun ; Jiong, Zhang
Author_Institution :
Sch. of Inf. Technol., Shandong Inst. of Commerce & Technol., Jinan, China
fYear :
2012
fDate :
12-14 Oct. 2012
Firstpage :
451
Lastpage :
453
Abstract :
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.
Keywords :
Web sites; classification; feature extraction; information retrieval; text analysis; Web page; classified text set; data processing; feature terms analyzing strategy; job relevant feature extracting method; latent useful pattern; text information; unstructured information analysis; unstructured information extraction; Agricultural products; Business; Educational institutions; Entropy; Feature extraction; Filtering algorithms; Information technology; concurrent terms; feature term extraction; maximum relevance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Business Computing and Global Informatization (BCGIN), 2012 Second International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4673-4469-2
Type :
conf
DOI :
10.1109/BCGIN.2012.123
Filename :
6382564
Link To Document :
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