Semi-supervised Learning for Automatic Prosodic Event Detection Using Co-training Algorithm

Most of previous approaches to automatic prosodic event detection are based on supervised learning, relying on the availability of a corpus that is annotated with the prosodic labels of interest in order to train the classification models. However, creating such resources is an expensive and time-consuming task. In this paper, we exploit semi-supervised learning with the co-training algorithm for automatic detection of coarse level representation of prosodic events such as pitch accents, intonational phrase boundaries, and break indices. We propose a confidence-based method to assign labels to unlabeled data and demonstrate improved results using this method compared to the widely used agreement-based method. In addition, we examine various informative sample selection methods. In our experiments on the Boston University radio news corpus, using only a small amount of the labeled data as the initial training set, our proposed labeling method combined with most confidence sample select...

• Type: Journal
• Year: 2009
• Where: ACL
• Authors: Je Hun Jeon, Yang Liu
• Keywords: ACL 2009, Computational Linguistics, Prosodic Event Detection, Prosodic Events, Unlabeled Data