Question Answering Systems 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 typically generates answers by querying a structured knowledge base (e.g., an organized database of knowledge) or 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 and supporting multiple domains. QA systems can be designed for restricted settings (e.g., closed-domain or closed-book) where knowledge is limited or memorized, or for broader settings (e.g., open-domain) where the system must retrieve and synthesize answers from large, general sources. Modern QA research and systems also extend to specialized question types (such as definitions, temporal/geospatial questions, and multilingual or multimodal questions involving text, images, audio, and video) and to interactive or reusable-answer approaches, often leveraging architectures like retriever-reader pipelines or end-to-end transformer-based models.

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Knowledge sources: structured databases and documents

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