A comparison of classifiers and document representations for the routing problem
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
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 pragmatics-based approach to ellipsis resolution
Computational Linguistics
A tripartite plan-based model of dialogue
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
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Speaker's intentions can be represented into domain actions (domain-independent speech acts and domain-dependent concept sequences). Therefore, domain action classification is very useful to a dialogue system that should catch user's intention in order to generate correct reaction. In this paper, we propose a neural network model to determine speech acts and concept sequences at the same time. To avoid biased learning problems, the proposed model uses low-level linguistic features and filters out uninformative features using χ2 statistic. In the experiment, the proposed model showed better performances than the previous work in speech act classification. Moreover, the proposed model showed meaningful results when the size of training corpus was small. Based on the experimental results, we believe that the proposed model will be more helpful to dialogue systems because it manages speech act classification and concept sequence classification at the same time. We also believe that the proposed model can alleviate sparse data problems in speech act classification.