Dialog Act Classification Using N-Gram Algorithms

Speech act classification remains one of the challenges in natural language processing. This paper evaluates a classification system that assigns one of twelve dialog acts to an utterance from the Map Task Corpus. The dialog act classification system chooses a dialog act based on n-grams from a training set. The system’s performance is comparable to other classification systems, like those using support vector machines. Performance is high given the fact that the system only considers an utterance out of context and from written input only. Moreover, the system’s performance is on par with human performance.