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

ACL ID P09-1061
Title Semi-supervised Learning for Automatic Prosodic Event Detection Using Co-training Algorithm
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2009
Authors

Most of previous approaches to automatic prosodic event detection are based on su- pervised learning, relying on the avail- ability 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 de- tection of coarse level representation of prosodic events such as pitch accents, in- tonational 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 selec- tion methods. I...