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