Editing Question answering (section)

==Types of question answering==
Question-answering research attempts to develop ways of answering a wide range of question types, including fact, list, [[definition]], how, why, hypothetical, semantically constrained, and cross-lingual questions.
* Answering questions related to an article in order to evaluate [[reading comprehension]] is one of the simpler form of question answering, since a given article is relatively short compared to the domains of other types of question-answering problems. An example of such a question is "What did Albert Einstein win the Nobel Prize for?" after an article about this subject is given to the system.
* ''Closed-book'' question answering is when a system has memorized some facts during training and can answer questions without explicitly being given a context. This is similar to humans taking closed-book exams. 
* ''Closed-domain'' question answering deals with questions under a specific domain (for example, medicine or automotive maintenance) and can exploit domain-specific knowledge frequently formalized in [[ontology (information science)|ontologies]]. Alternatively, "closed-domain" might refer to a situation where only a limited type of questions are accepted, such as questions asking for [[descriptive knowledge|descriptive]] rather than [[procedural knowledge|procedural]] information. Question answering systems {{vague|text=in the context of|date=April 2023}} machine reading applications have also been constructed in the medical domain, for instance {{vague|text=related to|date=April 2023}} Alzheimer's disease.<ref>Roser Morante, Martin Krallinger, Alfonso Valencia and  Walter Daelemans. [http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.366.3461 Machine Reading of Biomedical Texts about Alzheimer's Disease]. CLEF 2012 Evaluation Labs and Workshop. September 17, 2012</ref>
* ''[[Open domain#References|Open-domain]]'' question answering deals with questions about nearly anything and can only rely on general ontologies and world knowledge. Systems designed for open-domain question answering usually have much more data available from which to extract the answer. An example of an open-domain question is "What did Albert Einstein win the Nobel Prize for?" while no article about this subject is given to the system.

Another way to categorize question-answering systems is by the technical approach used. There are a number of different types of QA systems, including 
* [[rule-based system]]s, 
* statistical systems, and 
* [[Hybrid intelligent system|hybrid systems]]. 

Rule-based systems use a set of rules to determine the correct answer to a question. Statistical systems use statistical methods to find the most likely answer to a question. Hybrid systems use a combination of rule-based and statistical methods.