Natural language processing based question answering using vector space model

Abstract

Natural Language Processing (NLP) is a technique used to build computational models which deals with the interaction between computers and human languages. Question answering is expected to give the precise results for the query instead of a group of links or references which might contain an answer. The information in the web is basically growing and users are finding difficult to look for the answers through the search engines. In this research, a new approach is used to build the question answering system which uses vector space model by using unstructured data. In this proposed work, Keywords are generated by calculating the tf-idf score for each keyword and they are indexed to every file and query. The query vectors and Document vectors are compared and similarity values are generated using term frequency. Highest ranked documents are generated as per the similarity values and NER tagging is done to produce candidate answers from which best answer is chosen.