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Title: Text Categorization for an Online Tendering System

By Y. Wang, H. Zhang, B. Spencer and Y. YanY. Wang, H. Zhang, B. Spencer and Y. Yan

Abstract

Abstract: This paper investigates the application of text categorization (TC) in a setting exhibiting 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 reallife 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

Topics: ranking categorization
Year: 2008
OAI identifier: oai:CiteSeerX.psu:10.1.1.119.1385
Provided by: CiteSeerX
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