  In this paper, we describe a system to rank suspected answers to natural
language questions. We process both corpus and query using a new technique,
predictive annotation, which augments phrases in texts with labels anticipating
their being targets of certain kinds of questions. Given a natural language
question, an IR system returns a set of matching passages, which are then
analyzed and ranked according to various criteria described in this paper. We
provide an evaluation of the techniques based on results from the TREC Q&A
evaluation in which our system participated.
