Question Answering System

Question answering systems have become increasingly popular because they deliver users short, succinct answers instead of overloading them with a large number of irrelevant documents. Question Answering (QA) is a specialized form of information retrieval. Given a collection of documents, a Question Answering system attempts to retrieve the right answers to questions posed in natural language. In order for question answering systems to benefit from this vast store of useful knowledge, they must copy with large volumes of useless data. Question Answering systems (QA) uses natural language processing (NLP) techniques to process a question, then searches for the required information to identify the answer and presents the answer to the user. The Web is vastly larger in size and boasts incredible “data redundancy,” which renders it amenable to statistical techniques for answer extraction. The data-driven approach can yield high levels of performance and nicely complements traditional question answering techniques driven by information extraction. By organizing these resources and annotating them with natural language, we can successfully incorporate Web knowledge into question answering.