An experimental study of boosting model classifiers for chinese text categorization

Authors:
Yibing Geng;Guomin Zhu;Junrui Qiu;Jilian Fan;Jingchang Zhang
Affiliations:
Library, Second Military Medical University, Shanghai, P.R. China;Library, Second Military Medical University, Shanghai, P.R. China;Library, Second Military Medical University, Shanghai, P.R. China;Library, Second Military Medical University, Shanghai, P.R. China;Library, Second Military Medical University, Shanghai, P.R. China
Venue:
ICADL'04 Proceedings of the 7th international Conference on Digital Libraries: international collaboration and cross-fertilization
Year:
2004

Citing 11
Cited 1

Representation and learning in information retrieval

Representation and learning in information retrieval
Class-based n-gram models of natural language

Computational Linguistics
Towards language independent automated learning of text categorization models

SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
The nature of statistical learning theory

The nature of statistical learning theory
Improved Boosting Algorithms Using Confidence-rated Predictions

Machine Learning - The Eleventh Annual Conference on computational Learning Theory
BoosTexter: A Boosting-based Systemfor Text Categorization

Machine Learning - Special issue on information retrieval
An improved boosting algorithm and its application to text categorization

Proceedings of the ninth international conference on Information and knowledge management
Machine learning in automated text categorization

ACM Computing Surveys (CSUR)
Introduction to the special issue on computational linguistics using large corpora

Computational Linguistics - Special issue on using large corpora: I
Using register-diversified corpora for general language studies

Computational Linguistics - Special issue on using large corpora: II
Bagging, boosting, and C4.S

AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1

Digital library development in the asia pacific

ICADL'05 Proceedings of the 8th international conference on Asian Digital Libraries: implementing strategies and sharing experiences

Quantified Score

Hi-index	0.00

Visualization

Abstract

Text categorization is a crucial task of increasing importance. Our work focuses on the study of Chinese text categorization on the basis of Boosting model. We chose the People's Daily news from TREC5 as our benchmark datasets. A minor modification to AdaBoost algorithm (Freund and Schapire, 1996, 2000) was applied for this hypothesis. By way of using the F1 measure for its final evaluation, the results of the Boosting model (AdaBoost.MH) is proved to be effective and outperforms most of other algorithms reported for Chinese text categorization.