  We predict discourse segment boundaries from linguistic features of
utterances, using a corpus of spoken narratives as data. We present two methods
for developing segmentation algorithms from training data: hand tuning and
machine learning. When multiple types of features are used, results approach
human performance on an independent test set (both methods), and using
cross-validation (machine learning).
