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.