Question answering (QA) systems are a type of artificial intelligence that are designed to answer questions posed by humans in natural language. These systems are designed to understand the meaning and intent behind a question, and to provide a relevant and accurate answer in response.
QA systems are used in a wide range of applications, including search engines, virtual assistants, and customer service systems. They are designed to be able to understand and interpret a wide range of questions and to provide answers that are accurate and relevant to the question being asked.
There are several different types of QA systems, including rule-based systems, which rely on predefined rules and information to generate answers, and machine learning-based systems, which use data and algorithms to learn how to generate accurate answers. Machine learning-based QA systems are typically more flexible and adaptable than rule-based systems, as they can learn and improve over time based on new data and experiences.
- okbqa.org .. a community for advancement of question answering systems
- poweraqua .. a multi-ontology-based question answering system
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- Concordancers & Question Answering
- FREyA (Feedback, Refinement and Extended VocabularY Aggregation)
- Interactive Question Answering
- MIT START (Natural Language Question Answering System)
- NPCEditor (USC ICT)
- Ontology-based QA Systems
- OpenEphyra (Ephyra Question Answering System)
- PowerAnswer Question Answering System
- Question Ontology
- SQuAD (Stanford Question Answering Dataset)
- The YodaQA System
- Visual Question Answering