Ranking suspected answers to natural language questions using predictive   annotation
  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. 
Dragomir R. Radev (University of Michigan), John Prager (IBM TJ Watson   Research Center), Valerie Samn (Teachers College, C...