We describe work on automatically assigning classification labels to books using the Library
of Congress Classification scheme. This task is non-trivial due to the volume and variety of …

We tackle two different problems of text categorization (TC), namely feature selection and
classifier induction. Feature selection (FS) refers to the activity of selecting, from the set of r …

Text categorization or text classification (TC) has recently received increased research
attention from information retrieval and machine learning communities, this focus is driven …

Typical text classifiers learn from example and training documents that have been manually
categorized. In this research, our experiment dealt with the classification of news wire …

The sudden expansion of the web and the use of the internet has caused some research
fields to regain (or even increase) its old popularity. Of them, text categorization aims at …

Automatic text categorization is an important research area and has a potential for many text-
based applications including text routing and filtering. Typical text classifiers learn from …

In this paper, we present a linear text classification algorithm called CRF. By using category
relevance factors, CRF computes the feature vectors of training documents belonging to the …

In the Summer of 2001, at the International Joint Conference on Artificial Intelligence held in
Seattle, we presented a text categorization system based on an extension of the empirical …

The volume of the information available in the digital form continues to increase. Currently
there is about 1017 bits of information available on the Internet. There is an increasing need …

This paper examines the use of inductive learning to categorize natural language
documents into prede ned content categories. Categorization of text is of increasing …