A text categorization approach for match-making in online business tendering

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TypeArticle
Journal titleJournal of Business and Technology
Volume1
AbstractThis paper investigates the application of text categorization (TC) in an eBusiness setting that exhibits a large number of target categories with relatively few training cases, applied to a real-life online tendering system. This is an experiment paper showing our experiences in dealing with a real-life application using the conventional machine learning approaches for TC, namely, the Rocchio method, TF-IDF (term frequency-inverse document frequency), WIDF (weighted inverse document frequency), and naïve Bayes. In order to make the categorization results acceptable for industrial use, we made use of the hierarchical structure of the target categories and investigated the semi-automated ranking categorization.
Publication date
LanguageEnglish
AffiliationNational Research Council Canada; NRC Institute for Information Technology
Peer reviewedNo
NRC number48485
NPARC number8914075
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Record identifier8b1959ab-3981-494d-aae3-224a23c715de
Record created2009-04-22
Record modified2016-05-09
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