The nature of statistical learning theory
The nature of statistical learning theory
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Improving Text Classification by Shrinkage in a Hierarchy of Classes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Improving text categorization using the importance of sentences
Information Processing and Management: an International Journal
Analysis system of speech acts and discourse structures using maximum entropy model
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
An efficient statistical speech act type tagging system for speech translation systems
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
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A speech act is a linguistic action intended by a speaker. It is important to analyze the speech act for the dialogue understanding system because the speech act of an utterance is closely tied with the user's intention in the utterance. This paper proposes to use a speech acts hierarchy and a discourse stack for improving the accuracy of classifiers in speech acts analysis. We first adopt a hierarchical statistical technique called shrinkage to solve the data sparseness problem. In addition, we use a discourse stack in order to easily apply discourse structure information to the speech acts analysis. From the results of experiments, we observed that the proposed model made a significant improvement for Korean speech acts analysis. Moreover, we found that it can be more useful when training data is insufficient.