Semantic models for answer re-ranking in question answering

The task of Question Answering (QA) is to find correct answers to users' questions expressed in natural language. In the last few years non-factoid QA received more attention. It focuses on causation, manner and reason questions, where the expected answer has the form of a passage of text. The presence of question and answers corpora allows the adoption of Learning to Rank (MLR) algorithms in order to out- put a sensible ranking of the candidate answers. The importance and effectiveness of linguistically motivated features, obtained from syntax, lexical semantics and semantic role labeling, was shown in literature [2-4], but there are still several different possible semantic features that have not been taken into account so far and our goal is to find out if their use could lead to performance improvement. In particular features coming from Semantic Models (SM) like Distributional Semantic Models (DSMs), Explicit Semantic Analysis (ESA), Latent Dirichlet Allocation (LDA) induced topics have never been applied to the task so far. Based on the usefulness that those models show in other tasks, we think that SM can have a significant role in improving current state-of-the-art systems' performance in answer re-ranking.