Semantic Models for Re-ranking in Question Answering

This paper describes a research aimed at unveiling the role of Semantic Models in Question Answering. In these systems questions and answers are often expressed in quite different languages, so our objective is to bridge this “lexical chasm” adopting semantic representations. The aim of the research is to find out if Semantc Models are useful for this task and if they can improve the answer re-ranking performance. We have carried out an initial evaluation of a subset of the semantic models on the CLEF2010 QA dataset, showing their effectiveness. We also did a first attempt in combining them by means of Learning to Rank algorithms.