摘要: This article compares one-dimensional and multi-dimensional dialogue act tagsets used for automatic labeling of utterances. The influence tagset dimensionality on tagging accuracy is first discussed theoretically, then based empirical data from human annotations large scale resources, using four existing tagsets: damsl, swbd-damsl, icsi-mrda maltus. Dominant Function Approximation proposes that taggers could focus initially finding the main function each utterance, which empirically acceptable has significant practical relevance.
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