Using Active Learning in Text Classification of Quranic Sciences

Abstract

The key idea behind active learning is that if the learning method is allowed to choose the data to learn from, the amount of data needed for the training phase can be significantly reduced. Thus, the cost of manual annotating the data will be less, and the process of learning can be accelerated. Most of the studies on applying active learning methods to automatic text classification focused on requesting the label of a single unlabeled document in each iteration. Unlike English, There are very few researches done in this area for the Arabic text. In this paper, we present a novel active learning method for Arabic text classification using multi-class SVM. The proposed method selects a batch of informative samples for manually labeling by an expert. The experimental results show that employing our method can significantly reduce the need for labeled training data.