Development of a machine learnable discourse tagging tool

Authors:
Masahiro Araki;Yukihiko Kimura;Takuya Nishimoto;Yasuhisa Niimi
Affiliations:
Kyoto Institute of Technology, Matsugasaki Sakyo-ku Kyoto, Japan;Kyoto Institute of Technology, Matsugasaki Sakyo-ku Kyoto, Japan;Kyoto Institute of Technology, Matsugasaki Sakyo-ku Kyoto, Japan;Kyoto Institute of Technology, Matsugasaki Sakyo-ku Kyoto, Japan
Venue:
SIGDIAL '01 Proceedings of the Second SIGdial Workshop on Discourse and Dialogue - Volume 16
Year:
2001

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Abstract

We have developed a discourse level tagging tool for spoken dialogue corpus using machine learning methods. As discourse level information, we focused on dialogue act, relevance and discourse segment. In dialogue act tagging, we have implemented a transformation-based learning procedure and resulted in 70% accuracy in open test. In relevance and discourse segment tagging, we have implemented a decision-tree based learning procedure and resulted in about 75% and 72% accuracy respectively.