Ranking suspected answers to natural language questions using predictive annotation
Dragomir Radev, John M. Prager, Valerie Samn · 2000
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, our IR system returns a set of matching passages, which we then rank using a linear function of seven predictor variables. We provide an evaluation of the techniques based on results from the TREC Q&A evaluation in which our system participated.