Using SVM and Error-correcting Codes for M Meeting Cor

Accurate classification of dialog acts (DAs) is important for many spoken language applications. Different methods have been proposed such as hidden Markov models (HMM), maximum entropy (Maxent), graphical models, and support vector machines (SVMs). In this paper, we investigate using SVMs for multiclass DA classification in the ICSI meeting corpus. We evaluate (1) representing DA tagging directly as a multiclass task, and (2) combining multiple binary classifiers via error correction output codes (ECOC). For the ECOC combination, different code matrices are utilized (e.g., the identity matrix, exhaustive code, BCH code, and random code matrix). We also compare using SVMs with our previous Maxent model. We find that for DA tagging, using multiple binary SVMs via ECOC outperforms a direct multiclass SVM, but neither achieves better performance than the Maxent model, possibly because of the small class set and the features currently used in the task.