Text classification from unlabeled documents with bootstrapping and feature projection techniques

  • Authors:

  • Youngjoong Ko;Jungyun Seo

  • Affiliations:

  • Department of Computer Engineering, Dong-A University, 840 Hadan 2-dong, Saha-gu, Busan 604-714, Republic of Korea;Department of Computer Science and Program of Integrated Biotechnology, Sogang University, Sinsu-dong 1, Mapo-gu, Seoul 121-742, Republic of Korea

  • Venue:
  • Information Processing and Management: an International Journal
  • Year:
  • 2009

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Abstract

Many machine learning algorithms have been applied to text classification tasks. In the machine learning paradigm, a general inductive process automatically builds a text classifier by learning, generally known as supervised learning. However, the supervised learning approaches have some problems. The most notable problem is that they require a large number of labeled training documents for accurate learning. While unlabeled documents are easily collected and plentiful, labeled documents are difficultly generated because a labeling task must be done by human developers. In this paper, we propose a new text classification method based on unsupervised or semi-supervised learning. The proposed method launches text classification tasks with only unlabeled documents and the title word of each category for learning, and then it automatically learns text classifier by using bootstrapping and feature projection techniques. The results of experiments showed that the proposed method achieved reasonably useful performance compared to a supervised method. If the proposed method is used in a text classification task, building text classification systems will become significantly faster and less expensive.