  A simple and general method is described that can combine different knowledge
sources to reorder N-best lists of hypotheses produced by a speech recognizer.
The method is automatically trainable, acquiring information from both positive
and negative examples. Experiments are described in which it was tested on a
1000-utterance sample of unseen ATIS data.
