Natural Language Processing based Question Answering Techniques: A Survey

Abstract

The rapid development in the field of information science and the increase in the usage of information retrieval strategies have empowered users to retrieve more accurate information. The availability of information in a different format and across different has presented colossal difficulties for information retrieval using information retrieval techniques. In this paper, an attempt is made at highlighting the question and answering (QA) systems that provide users the platform to express their question using the Natural Language and also retrieve the response from such systems in Natural Language. The four important modules of any QA system are the Natural Language Question (NLQ) processing, document processing, passage processing, and answer processing. Fundamentally, most QA systems combined several techniques from other fields such as information retrieval, knowledge representation, and natural language processing to process NLQ and present the most insightful response based on the stored document. This paper gives a thorough review of the various survey on the QA systems frameworks, their methodologies, types, approaches, and challenges of QA systems.