ISSD-93 Selected papers presented at the international symposium on Spoken dialogue
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Dialogue act modeling for automatic tagging and recognition of conversational speech
Computational Linguistics
A maximum entropy approach to identifying sentence boundaries
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
A tripartite plan-based model of dialogue
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
| Hi-index | 0.00 |
To generate correct reactions, a dialogue system should identify domain actions indicated by users' utterances because speaker intentions can be captured by the domain actions. In this paper, we propose a domain action classification model to determine speech acts (general intentions) and concept sequences (semantic focuses) at the same time in a schedule management domain. To avoid biased learning problems, the proposed model uses low-level linguistic features and filters out uninformative features using statistic. Then, the proposed model determines domain actions using a maximum entropy model. In the experiment, the proposed model showed better performances than previous works in speech act classification. In addition, the proposed model showed high performances in concept sequence classification. Based on these experimental results, we believe that the proposed model will be more helpful to a dialogue system than previous speech act classification models because it can return speech acts and concept sequences at the same time on the same framework.