Towards Scalable Speech Act Recognition in Twitter: Tackling Insufficient Training Data
Renxian Zhang, Dehong Gao, Wenjie Li · 2012
Recognizing speech act types in Twitter is of much theoretical interest and practical use. Our previous research did not adequately address the deficiency of training data for this multi-class learning task. In this work, we set out by assuming only a small seed training set and experiment with two semi-supervised learning schemes, transductive SVM and graph-based label propagation, which can leverage the knowledge about unlabeled data. The efficacy of semi-supervised learning is established by our extensive experiments, which also show that transductive SVM is more suitable than graph-based label propagation for our task. The empirical findings and detailed evidences can contribute to scalable speech act recognition in Twitter. 1.