FALCON: Boosting Knowledge for Answer Engines

Abstract

This paper presents the features of Falcon, an answer engine that integrates dierent forms of syntactic, semantic and pragmatic knowledge for the goal of achieving better performance. The answer engine handles question reformulations, finds the expected answer type from a large hierarchy that incorporates the WordNet semantic net and extracts answers after performing unifications on the semantic forms of the question and its candidate answers. To rule out erroneous answers, it provides a justification option, implemented as an abductive proof. In TREC-9, Falcon generated a score of 58% for short answers and 76% for long answers.