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.
Cite
CITATION STYLE
Araki, M., Kimura, Y., Nishimoto, T., & Niimi, Y. (2001). Development of a machine learnable discourse tagging tool. In Proceedings of the SIGDIAL 2001 Workshop - 2nd Annual Meeting of the Special Interest Group on Discourse and Dialogue. Association for Computational Linguistics (ACL). https://doi.org/10.3115/1118078.1118081
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