Web Mining for Unsupervised Classification

Web Mining for Unsupervised Classification

Data acquisition is a major concern in text classification. The excessive human efforts required by conventional methods to build up quality training collection might not always be available to research workers. In this paper, we look into possibilities to automatically collect training data by sampling the Web with a set of given class names. The basic idea is to populate appropriate keywords and submit them as queries to search engines for acquiring training data. Two methods are presented in this study: One method is based on sampling the common concepts among the classes, and the other based on sampling the discriminative concepts for each class. A series of experiments were carried out independently on two different datasets, and the result shows that the proposed methods significantly improve classifier performance even without using manually labeled training data. Our strategy for retrieving Web samples, we find that, is substantially helpful in conventional document classification in terms of accuracy and efficiency.