Text categorization using machine lerning

With the rapid spread of the Internet and the increase in on-line information, the technology for automatically classifying huge amounts of diverse text information has come to play a very important role. In the 1990s, the performance of computers improved sharply and it became possible to handle large quantities of text data. This led to the use of the machine learning approach, which is a method of creating classifiers automatically from the text data given in a category label. This approach provides excellent accuracy, reduces labor, and ensures conservative use of resources. This paper discusses the following three points related to text classification using machine learning. 1. How to perform highly precise classification by using a large number of word attributes (Chapter 3). 2. How to utilize the distribution of unlabeled examples for high precision when there are few labeled training examples (Chapter 4). 3. How to achieve a highly precise and efficient classification by assuming the existence of sub-categories and using active labeling (Chapter 5). ∗ Doctor’s Thesis, Department of Information Processing, Graduate School of Information Science, Nara Institute of Science and Technology, NAIST-IS-DT0061207, February 5, 2002.