Question Answering (QA) is a type of natural language processing task that aims to provide an answer to an open-ended question, using printed or spoken natural language. Unlike other types of searches, such as keyword searches, question-answering systems allow users to ask a question in natural language, and receive a direct answer from stored data.
QA typically involves the use of natural language processing (NLP) techniques to break down and understand the question, then map it to an existing pool of information. These QA systems are designed to understand natural language and the meaning of words in context, so that queries can be answered more accurately.
QA systems typically employ structured natural language processing, wherein a selected answer must be found among known possible answers within a limited domain. This is done by scanning a stored database for relevant information, and then narrowing down the results until a viable answer is found. Some systems also incorporate natural language generation tools to provide a more cohesive response to the natural language question.
QA systems are widely used in the digital realm, both on the internet and in consumer electronic devices. Online search engines, such as Google, use natural language processing for QA when trying to answer questions posed on websites. SIRI, Alexa, and other AI-powered voice assistants are also becoming popular QA tools.
The goal of QA is to allow users to access information more quickly and accurately then they would with traditional keyword searches. Through ongoing advancements in AI-powered NLP, the accuracy and usability of QA systems are likely to continue to increase over time.
