Text classification and keyword extraction by learning decision trees

Summary form only given. The authors propose a completely new approach to the problem of text classification and automatic keyword extraction by using machine learning techniques. They introduce a class of representations for classifying text data based on decision trees, and present an algorithm for learning it inductively. The algorithm does not need any natural language processing technique, and is robust to noisy data. It is shown that the learning algorithm can be used for automatic extraction of keywords for text retrieval and automatic text categorization. Some experimental results on the use of the algorithm are reported