Co-training for Predicting Emotions with Spoken Dialogue Data
- Beatriz Maeireizo ,
- Diane Litman ,
- Rebecca Hwa
Companion Proceedings of ACL-04, the 42nd Annual Meeting of the Association for Computational Linguistics |
Natural Language Processing applications often require large amounts of annotated training data, which are expensive to obtain. In this paper we investigate the applicability of Co-training to train classifiers that predict emotions in spoken dialogues. In order to do so, we have first applied the wrapper approach with Forward Selection and Na�ve Bayes, to reduce the dimensionality of our feature set. Our results show that Co-training can be highly effective when a good set of features are chosen.