JASON CHAN
School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia
IRENA KOPRINSKA
School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia
JOSIAH POON
School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia

Traditional supervised classification algorithms require a large number of labelled examples to perform accurately. Semi-supervised classification algorithms attempt to overcome this major limitation by also using unlabelled examples. Unlabelled examples have also been used to improve nearest neighbour text classification in a method called bridging. In this paper, we propose the use of bridging in a semi-supervised setting. We introduce a new bridging algorithm that can be used as a base classifier in most semi-supervised approaches. We empirically show that the classification performance of two semi-supervised algorithms, self-learning and co-training, improves with the use of our new bridging algorithm in comparison to using the standard classifier, JRipper. We propose a similarity metric for short texts and also study the performance of self-learning with a number of instance selection heuristics.

Semi-supervised learning; bridging