  We present an empirical investigation of various ways to automatically
identify phrases in a tagged corpus that are useful for dialogue act tagging.
We found that a new method (which measures a phrase's deviation from an
optimally-predictive phrase), enhanced with a lexical filtering mechanism,
produces significantly better cues than manually-selected cue phrases, the
exhaustive set of phrases in a training corpus, and phrases chosen by
traditional metrics, like mutual information and information gain.
