Semantic Models for Question Answering

The research presented in this paper focuses on the adoption of semantic models for Question Answering (QA) systems. We propose a framework which exploits semantic technologies to analyze the question, retrieve and rank relevant passages. It exploits: (a) Natural Language Processing algorithms for the analysis of questions and candidate answers both in English and Italian; (b) Information Retrieval (IR) probabilistic models for retrieving candidate answers and (c) Machine Learning methods for question classification. The data source for the answers is an unstructured text document collection stored in search indices. The aim of the research is to improve the system performances by introducing semantic models in every step of the answering process.