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Question Answering (QA) is a computer science discipline within the fields of information retrieval and natural language processing (NLP), which is concerned with building systems that automatically answer questions posed by humans in a natural language. A QA implementation, usually a computer program, may construct its answers by querying a structured database of knowledge or information, usually a knowledge base. More commonly, QA systems can pull answers from an unstructured collection of natural language documents. Some examples of natural language document collections used for QA systems include: * a local collection of reference texts * internal organization documents and web pages * compiled newswire reports * a set of Wikipedia pages * a subset of World Wide Web pages QA research attempts to deal with a wide range of question types including: fact, list, definition, ''How'', ''Why'', hypothetical, semantically constrained, and cross-lingual questions. * ''Closed-domain'' question answering deals with questions under a specific domain (for example, medicine or automotive maintenance), and can be seen as an easier task because NLP systems can exploit domain-specific knowledge frequently formalized in ontologies. Alternatively, ''closed-domain'' might refer to a situation where only a limited type of questions are accepted, such as questions asking for descriptive rather than procedural information. QA systems in the context of machine reading applications have also been constructed in the medical domain, for instance related to Alzheimers disease 〔Roser Morante , Martin Krallinger , Alfonso Valencia and Walter Daelemans. Machine Reading of Biomedical Texts about Alzheimer's Disease. CLEF 2012 Evaluation Labs and Workshop. September 17, 2012〕 * ''Open-domain'' question answering deals with questions about nearly anything, and can only rely on general ontologies and world knowledge. On the other hand, these systems usually have much more data available from which to extract the answer. ==History== Two early QA systems were BASEBALL and LUNAR. BASEBALL answered questions about the US baseball league over a period of one year. LUNAR, in turn, answered questions about the geological analysis of rocks returned by the Apollo moon missions. Both QA systems were very effective in their chosen domains. In fact, LUNAR was demonstrated at a lunar science convention in 1971 and it was able to answer 90% of the questions in its domain posed by people untrained on the system. Further restricted-domain QA systems were developed in the following years. The common feature of all these systems is that they had a core database or knowledge system that was hand-written by experts of the chosen domain. The language abilities of BASEBALL and LUNAR used techniques similar to ELIZA and DOCTOR, the first chatterbot programs. SHRDLU was a highly successful question-answering program developed by Terry Winograd in the late 60s and early 70s. It simulated the operation of a robot in a toy world (the "blocks world"), and it offered the possibility to ask the robot questions about the state of the world. Again, the strength of this system was the choice of a very specific domain and a very simple world with rules of physics that were easy to encode in a computer program. In the 1970s, knowledge bases were developed that targeted narrower domains of knowledge. The QA systems developed to interface with these expert systems produced more repeatable and valid responses to questions within an area of knowledge. These expert systems closely resembled modern QA systems except in their internal architecture. Expert systems rely heavily on expert-constructed and organized knowledge bases, whereas many modern QA systems rely on statistical processing of a large, unstructured, natural language text corpus. The 1970s and 1980s saw the development of comprehensive theories in computational linguistics, which led to the development of ambitious projects in text comprehension and question answering. One example of such a system was the Unix Consultant (UC), developed by Robert Wilensky at U.C. Berkeley in the late 1980s. The system answered questions pertaining to the Unix operating system. It had a comprehensive hand-crafted knowledge base of its domain, and it aimed at phrasing the answer to accommodate various types of users. Another project was LILOG, a text-understanding system that operated on the domain of tourism information in a German city. The systems developed in the UC and LILOG projects never went past the stage of simple demonstrations, but they helped the development of theories on computational linguistics and reasoning. Recently, specialized natural language QA systems have been developed, such as (EAGLi ) for health and life scientists. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「question answering」の詳細全文を読む スポンサード リンク
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