Question Answering Learning Path

Definition and scope of question answering

Question answering (QA) is a computer science discipline within information retrieval and natural language processing (NLP) focused on building systems that automatically answer questions posed by humans in natural language. A QA system may generate answers by querying structured knowledge bases (e.g., curated databases) or, more commonly, by extracting answers from unstructured collections of natural language documents such as reference texts, news articles, Wikipedia, and other web pages. The scope of QA includes answering many kinds of questions (e.g., fact, list, definition, how/why, hypothetical, and cross-lingual) and supporting different interaction and reasoning needs. QA can be implemented in various settings such as closed-book (memorized facts without explicit context), closed-domain (restricted to a specific subject area), and open-domain (answers about nearly anything using general knowledge and large document collections). Modern QA research and systems also extend to specialized domains like mathematics, temporal/geospatial questions, definitions/terminology, biographies, and multimodal content (e.g., audio, images, and video), often using architectures such as retriever-reader pipelines or end-to-end transformer-based models.

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Knowledge sources: structured databases and documents

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