Korean Speech Act Analysis System Using Hidden Markov Model with Decision Trees
Songwook Lee, Jungyun Seo · International Journal of Computer Processing Of Languages · 2002
Analyzing speech act is important for understanding the intention of a speaker and the flow of dialogue in discourse analysis. This study analyzes speech act by using syntactic pattern information which is tagged in dialogue corpus. We apply bigram Hidden Markov Model(HMM) to the task of computing speech act. We compute speech act probabilities for each utterance with forward algorithm. When computing the speech act probabilities to find the best path in HMM, there arises a sparse data problem. We smoothed probabilities through class probabilities of decision trees.