Notes:
Question answering systems, also known as QA systems, are computer programs that are designed to answer questions posed by users in natural language. These systems use artificial intelligence and natural language processing techniques to understand the user’s question and generate a response that is relevant and accurate.
Question answering systems can be classified into two main types: rule-based systems and machine learning-based systems. Rule-based systems use a set of pre-defined rules and algorithms to generate answers to questions. These systems can provide fast and accurate answers to questions that are straightforward and well-structured, but may struggle with more complex or open-ended questions.
Machine learning-based systems, on the other hand, use machine learning algorithms to learn from large datasets of questions and answers. These systems can generate more flexible and personalized responses to questions, but require a larger amount of training data and may take longer to generate answers.
Question answering systems have a wide range of applications, including search engines, virtual assistants, and customer service chatbots. They can help users find information quickly and accurately, and can improve the user experience by providing personalized and relevant answers to their questions.
- Question and answer system
- Question and answer systems
- Question answer system
- Question answer systems
- Question answering system
- Question answering systems
Wikipedia:
See also:
DBpedia & Question Answering Systems | FREyA (Feedback, Refinement and Extended VocabularY Aggregation) 2016 | Interactive Question Answering | Pythia Question Answering System | Question Answering Module | Question-Answer Pairs & Dialog Systems | SPARQL & Natural Language Question Answering 2016 | YourQA (York Question Answering System)
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