Editing Question answering (section)

==History==
Two early question answering systems were BASEBALL<ref>{{cite journal|last1=GREEN JR|first1=Bert F|title=Baseball: an automatic question-answerer|journal=Western Joint IRE-AIEE-ACM Computer Conference|date=1961|pages=219–224|display-authors=etal|url=https://web.stanford.edu/class/linguist289/p219-green.pdf}}</ref> and LUNAR.<ref>{{cite journal|last1=Woods|first1=William A|last2=Kaplan|first2=R.|title=Lunar rocks in natural English: Explorations in natural language question answering|journal=Linguistic Structures Processing 5|date=1977|volume=5|pages=521–569}}</ref> BASEBALL answered questions about Major League Baseball over a period of one year{{ambiguous|reason=it answered questions for a year, or it answered questions about a year's worth of data, or something else?|date=April 2023}}. LUNAR answered questions about the geological analysis of rocks returned by the Apollo Moon missions. Both question answering systems were very effective in their chosen domains. LUNAR was demonstrated at a lunar science convention in 1971 and it was able to answer 90% of the questions in its domain that were posed by people untrained on the system. Further restricted-domain question answering 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 successful question-answering program developed by [[Terry Winograd]] in the late 1960s and early 1970s. It simulated the operation of a robot in a toy world (the "blocks world"), and it offered the possibility of asking the robot questions about the state of the world. 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 base]]s were developed that targeted narrower domains of knowledge. The question answering systems developed to interface with these [[expert system]]s produced {{clarify|text=more repeatable|date=April 2023}} and valid responses to questions within an area of knowledge. These expert systems closely resembled modern question answering systems except in their internal architecture. Expert systems rely heavily on expert-constructed and organized [[knowledge base]]s, whereas many modern question answering 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 was the Unix Consultant (UC), developed by [[Robert Wilensky]] at [[University of California, Berkeley|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 [[natural-language understanding|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.

Specialized natural-language question answering systems have been developed, such as EAGLi for health and life scientists.<ref>{{Cite web|title=EAGLi platform - Question Answering in MEDLINE|url=https://candy.hesge.ch/EAGLi/|access-date=2021-12-02|website=candy.hesge.ch}}</ref>