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The premise of symbolic NLP is well-summarized by [[John Searle]]'s [[Chinese room]] experiment: Given a collection of rules (e.g., a Chinese phrasebook, with questions and matching answers), the computer emulates natural language understanding (or other NLP tasks) by applying those rules to the data it confronts.
* '''1950s''': The [[Georgetown-IBM experiment|Georgetown experiment]] in 1954 involved fully [[automatic translation]] of more than sixty Russian sentences into English. The authors claimed that within three or five years, machine translation would be a solved problem.<ref>{{cite web|author=Hutchins, J.|year=2005|url=http://www.hutchinsweb.me.uk/Nutshell-2005.pdf|title=The history of machine translation in a nutshell}}{{self-published source|date=December 2013}}</ref> However, real progress was much slower, and after the [[ALPAC|ALPAC report]] in 1966, which found that ten years of research had failed to fulfill the expectations, funding for machine translation was dramatically reduced. Little further research in machine translation was conducted in America (though some research continued elsewhere, such as Japan and Europe<ref>"ALPAC: the (in)famous report", John Hutchins, MT News International, no. 14, June 1996, pp. 9–12.</ref>) until the late 1980s when the first [[statistical machine translation]] systems were developed.t
* '''1960s''': Some notably successful natural language processing systems developed in the 1960s were [[SHRDLU]], a natural language system working in restricted "[[blocks world]]s" with restricted vocabularies, and [[ELIZA]], a simulation of a [[Rogerian psychotherapy|Rogerian psychotherapist]], written by [[Joseph Weizenbaum]] between 1964 and 1966. Using almost no information about human thought or emotion, ELIZA sometimes provided a startlingly human-like interaction. When the "patient" exceeded the very small knowledge base, ELIZA might provide a generic response, for example, responding to "My head hurts" with "Why do you say your head hurts?". [[Ross Quillian]]'s successful work on natural language was demonstrated with a vocabulary of only ''twenty'' words, because that was all that would fit in a computer memory at the time.<ref>{{Harvnb|Crevier|1993|pp=146–148}}, see also {{Harvnb|Buchanan|2005|p=56}}: "Early programs were necessarily limited in scope by the size and speed of memory"</ref>
* '''1970s''': During the 1970s, many programmers began to write "conceptual [[ontology (information science)|ontologies]]", which structured real-world information into computer-understandable data. Examples are MARGIE (Schank, 1975), SAM (Cullingford, 1978), PAM (Wilensky, 1978), TaleSpin (Meehan, 1976), QUALM (Lehnert, 1977), Politics (Carbonell, 1979), and Plot Units (Lehnert 1981). During this time, the first [[chatterbots]] were written (e.g., [[PARRY]]).
* '''1980s''': The 1980s and early 1990s mark the heyday of symbolic methods in NLP. Focus areas of the time included research on rule-based parsing (e.g., the development of [[Head-driven phrase structure grammar|HPSG]] as a computational operationalization of [[generative grammar]]), morphology (e.g., two-level morphology<ref>{{citation|last=Koskenniemi|first=Kimmo|title=Two-level morphology: A general computational model of word-form recognition and production|url=http://www.ling.helsinki.fi/~koskenni/doc/Two-LevelMorphology.pdf|year=1983|publisher=Department of General Linguistics, [[University of Helsinki]]|author-link=Kimmo Koskenniemi}}</ref>), semantics (e.g., [[Lesk algorithm]]), reference (e.g., within Centering Theory<ref>Joshi, A. K., & Weinstein, S. (1981, August). [https://www.ijcai.org/Proceedings/81-1/Papers/071.pdf Control of Inference: Role of Some Aspects of Discourse Structure-Centering]. In ''IJCAI'' (pp. 385–387).</ref>) and other areas of natural language understanding (e.g., in the [[Rhetorical structure theory|Rhetorical Structure Theory]]). Other lines of research were continued, e.g., the development of chatterbots with [[Racter]] and [[Jabberwacky]]. An important development (that eventually led to the statistical turn in the 1990s) was the rising importance of quantitative evaluation in this period.<ref>{{Cite journal|last1=Guida|first1=G.|last2=Mauri|first2=G.|date=July 1986|title=Evaluation of natural language processing systems: Issues and approaches|journal=Proceedings of the IEEE|volume=74|issue=7|pages=1026–1035|doi=10.1109/PROC.1986.13580|s2cid=30688575|issn=1558-2256}}</ref>
=== Statistical NLP (1990s–present) ===
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