A predictive framework for retrieving the best answer

Abstract

In a question answering (QA) system, each user interaction with the system is different and since there are a variety of arguably correct answers to complex questions, identifying factors for improving the quality of the retrieved answer is difficult. This research aims to develop a framework that identifies predictive variables for the best quality answer in a QA system. It was found that accuracy, completeness and relevance were predictors of best answer. We believe that these findings can serve to guide future developments in the answer extraction modules in the QA systems.