Asian Journal of Information Technology
ISSN: Online 1993-5994ISSN: Print 1682-3915
Abstract
Text classification is becoming more and more important with the rapid growth of on-line information available. It was observed that the quality of training corpus impacts the performance of the trained classifier. This paper proposes an approach to build high-quality training corpuses for better classification performance by first exploring the properties of training corpuses, and then giving an algorithm for constructing training corpuses semi-automatically. Preliminary experimental results validate our approach: classifiers based on the training corpuses constructed by our approach can achieve good performance while the training corpus` size is significantly reduced. Our approach can be used for building efficient and lightweight classification systems.
How to cite this article:
Jihong Guan and Shuigeng Zhou . An Effective Approach to the Evaluation and Construction of Training Corpus for Text Classification.
DOI: https://doi.org/10.36478/ajit.2005.33.40
URL: https://www.makhillpublications.co/view-article/1682-3915/ajit.2005.33.40