jithin pradeep Cognitive Research Scientist | AI and Mixed reality Enthusiast

Question Answering systems

Question Answering systems are usually referred to a computer program that are capable of answering questions posed by humans in a natural language. It may construct its answers by querying a structured repositories of knowledge or information, usually a knowledge base. More commonly, QA systems can pull answers from an unstructured collection of natural language documents which may include a group of reference documents, webpages or even set of Wikipedia pages.

Natural language processing methods are used to both process the question and indexed documents or the text corpus from which answers are extracted. Most of QA systems use the World Wide Web as their corpus of knowledge, however, these systems do not yield a human-like answer, but rather employ narrow approaches (keyword-based techniques, templates, etc.) to produce a list of document extracts encompassing the possible answer.

Generally QA systems included a question classifier unit that determines the type of question and answer. The system apply progressively complex NLP methods in order to steadily reduce the volume of text. Based on which document sets or extracts are generated that are likely to contain the answer, and then the reselect small text fragments that contain strings of the same type as the expected answer are filtered out. Scoring are provided to all candidate answer found.

Types of question encountered by a Question Answering systems

Two main category of question are based on the choice of domain of the application for which the system is built, namely the