Active Learning Based on Transfer Learning Techniques for Text Classification

Abstract

Text preprocessing is a common task in machine learning applications that involves hand-labeling sets. Although automatic and semi-automatic annotation of text data is a growing field, researchers need to develop models that use resources as efficiently as possible for a learning task. The goal of this work was to learn faster with fewer resources. In this paper, the combination of active and transfer learning was examined with the purpose of developing an effective text categorization method. These two forms of learning have proven their efficiency and capacity to train correct models with substantially less training data. We considered three types of criteria for selecting training points: random selection, uncertainty sampling criterion and active transfer selection. Experimental evaluation was performed on five data sets from different domains. The findings of the experiments suggest that by combining active and transfer learning, the algorithm performs better with fewer labels than random selection of training points.