Distributional Semantics for Answer Re-ranking in Question Answering

This paper investigates the role of Distributional Semantic Models (DSMs) into a Question Answering (QA) system. Our purpose is to exploit DSMs for answer re-ranking in QuestionCube, a framework for building QA systems. DSMs model words as points in a geometric space, also known as semantic space. Words are similar if they are close in that space. Our idea is that DSMs approaches can help to compute relatedness between users’ questions and candidate answers by exploiting paradigmatic relations between words, thus providing better answer reranking. Results of the evaluation, carried out on the CLEF2010 QA dataset, prove the effectiveness of the proposed approach

Distributional Semantics for Answer Re-ranking in Question Answering ⋆

Abstract. This paper investigates the role of Distributional Semantic Models (DSMs) into a Question Answering (QA) system. Our purpose is to exploit DSMs for answer re-ranking in QuestionCube, a framework for building QA systems. DSMs model words as points in a geometric space, also known as semantic space. Words are similar if they are close in that space. Our idea is that DSMs approaches can help to compute relatedness between users ’ questions and candidate answers by exploiting paradigmatic relations between words, thus providing better answer reranking. Results of the evaluation, carried out on the CLEF2010 QA dataset, prove the effectiveness of the proposed approach.