Youngeun Koo  Jiyoun Kim  Munpyo Hong  Youngkil Kim

10.5626/JOK.2018.45.8.807

Speech act Automatic speech act classification Linguistic feature Machine Learning

Speech act is a speaker’s intention of utterance in communication. To communicate successfully, we need to figure out speech act of a speaker’s utterance correctly. This paper proposed linguistic features of an utterance that affect speech act classification by analyzing Korean tutorial dialogue. Ultimately we hope this enables automatic speech act classification. Thirteen linguistically motivated features are suggested in this paper and verified with WEKA 3.8.1. The accuracy of the proposed linguistically motivated features of speech act classification reached 70.03%. Approximately 30%p of accuracy has improved compared to a baseline, using unigram and bigram as the only features of speech act classification.