Novel Scoring System for Identify Accurate Answers for Factoid Questions

Question and Answer System (QAS) are some of the many challenges for natural language understanding and interfaces. In this paper we have develop a new scoring mathematical model that works on the five types of questions. The question text failures are first extracted and a score is found based on its structure with respect to its template structure and then answer score is calculated again the question as well as paragraph. A name entity recognizer and a Part of Speech tagger are applied on each of these words to encode necessary of information. After that the text to finally reach at the index of the most probable answer with respect to question. In this the entropy algorithm is used to find the exact answer.