Margin-based active learning and background knowledge in text mining

Abstract

Text mining, also known as intelligent text analysis, text data mining or knowledge-discovery in text, refers generally to the process of extracting interesting and nontrivial information and knowledge from text. One of the main problems with text mining and classification systems is the lack of labeled data, as well as the cost of labeling unlabeled data (Kiritchenko and Matwin 2001). Thus, there is a growing interest in exploring the use of unlabeled data as a way to improve classification performance in text classification. The ready availability of this kind of data in most applications makes it an appealing source of information. In this work we evaluate the benefits of introducing unlabeled data in a support vector machine automatic text classifier. We further evaluate the possibility of learning actively and propose a method for choosing the samples to be learned.