  In this paper we describe the linguistic processor of a spoken dialogue
system. The parser receives a word graph from the recognition module as its
input. Its task is to find the best path through the graph. If no complete
solution can be found, a robust mechanism for selecting multiple partial
results is applied. We show how the information content rate of the results can
be improved if the selection is based on an integrated quality score combining
word recognition scores and context-dependent semantic predictions. Results of
parsing word graphs with and without predictions are reported.
