Semantic learning for question and answering systems

Abstract

Semantic similarity methods play a significant role in different areas including community mining, document clustering, automatic metadata extraction, information retrieval, document clustering, synonym extraction. In the recent past semantic similarity has been approved as a feasible and scalable alternative to grasp natural language. This review paper presents the existing techniques in semantic similarity and how these techniques are applied in question and answering systems. Furthermore, this illustrates the drawbacks of current techniques and recommendations will be presented to improve semantic learning for question and answering systems.