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• W2158342607 abstract "A dialogue-based interface for information systems is considered a potentially very useful approach to information access. A key step in computer processing of natural-language dialogues is dialogue-act (DA) recognition. In this paper, we apply a feature-based classification approach for DA recognition, by using the maximum entropy (ME) method to build a classifier for labeling utterances with DA tags. The ME method has the advantage that a large number of heterogeneous features can be flexibly combined in one classifier, which can facilitate feature selection. A unique characteristic of our approach is that it does not need to model the prior probability of DAs directly, and thus avoids the use of a discourse grammar. This simplifies the implementation of the classifier and improves the efficiency of DA recognition, without sacrificing the classification accuracy. We evaluate the classifier using a large data set based on the Switchboard corpus. Encouraging performance is observed; the highest classification accuracy achieved is 75.03%. We also propose a heuristic to address the problem of sparseness of the data set. This problem has resulted in poor classification accuracies of some DA types that have very low occurrence frequencies in the data set. Preliminary evaluation shows that the method is effective in improving the macroaverage classification accuracy of the ME classifier. © 2008 Wiley Periodicals, Inc." @default.
• W2158342607 created "2016-06-24" @default.
• W2158342607 creator A5004317456 @default.
• W2158342607 creator A5029313981 @default.
• W2158342607 creator A5058103634 @default.
• W2158342607 creator A5081054998 @default.
• W2158342607 date "2008-04-01" @default.
• W2158342607 modified "2023-09-23" @default.
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