We propose a speech-act analysis method for Korean dialogue using Support Vector Machines (SVM). We use a lexical word, its part of speech (POS) tags, and bigrams of POS tags as utterance feature and the contexts of the previous utterance as context feature. We select informative features by
statistic. After training SVMs with the selected features, SVM classifiers determine the speech-act of each utterance. In experiment, we acquired overall 90.5% of accuracy with dialogue corpus for hotel reservation domain.