This paper investigates the role of Distributional
Semantic Models (DSMs) in Question Answering (QA), and
specifically in a QA system called QuestionCube. QuestionCube is
a framework for QA that combines several techniques to retrieve
passages containing the exact answers for natural language questions.
It exploits Information Retrieval models to seek candidate
answers and Natural Language Processing algorithms for the
analysis of questions and candidate answers both in English and
Italian. The data source for the answer is an unstructured text
document collection stored in search indices.
In this paper we propose to exploit DSMs in the QuestionCube
framework. In DSMs words are represented as mathematical
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. Results of an experimental evaluation
carried out on CLEF2010 QA dataset, prove the effectiveness of
the proposed approach.