Optimizing Features for Dialogue Act Classification

Natural language dialogue is an important component of interaction between ordinary users and complex computer applications. Short Text Semantic Similarity algorithms have been developed to improve the efficiency of producing sophisticated dialogue systems. Such algorithms are currently unable to discriminate between different dialogue acts (assertions, questions, instructions etc.), requiring the addition of efficient dialogue act classifiers to enhance them. The Slim Function Word Classifier (SFWC) has proved promising, particularly in its computational simplicity. This study optimizes the SFWC by clustering function word features using grammatical principles. Experiments show a significant improvement in classification accuracy for a selection of sentence forms which were challenging for the unoptimized SFWC. Results are expected to be applicable to many intelligent text processing applications ranging from question answering to meeting summarization.