However, most other systems depended on corpora specifically developed for the tasks implemented by these systems, which was (and often continues to be) a major limitation in the success of these systems. These systems were able to take advantage of existing multilingual textual corpora that had been produced by the Parliament of Canada and the European Union as a result of laws calling for the translation of all governmental proceedings into all official languages of the corresponding systems of government. 1990s: Many of the notable early successes on statistical methods in NLP occurred in the field of machine translation, due especially to work at IBM Research, such as IBM alignment models.transformational grammar), whose theoretical underpinnings discouraged the sort of corpus linguistics that underlies the machine-learning approach to language processing. This was due to both the steady increase in computational power (see Moore's law) and the gradual lessening of the dominance of Chomskyan theories of linguistics (e.g. Starting in the late 1980s, however, there was a revolution in natural language processing with the introduction of machine learning algorithms for language processing. Up to the 1980s, most natural language processing systems were based on complex sets of hand-written rules. An important development (that eventually led to the statistical turn in the 1990s) was the rising importance of quantitative evaluation in this period. Other lines of research were continued, e.g., the development of chatterbots with Racter and Jabberwacky. Focus areas of the time included research on rule-based parsing (e.g., the development of HPSG as a computational operationalization of generative grammar), morphology (e.g., two-level morphology ), semantics (e.g., Lesk algorithm), reference (e.g., within Centering Theory ) and other areas of natural language understanding (e.g., in the Rhetorical Structure Theory). 1980s: The 1980s and early 1990s mark the heyday of symbolic methods in NLP.During this time, the first chatterbots were written (e.g., PARRY). 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). 1970s: During the 1970s, many programmers began to write "conceptual ontologies", which structured real-world information into computer-understandable data.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?". Using almost no information about human thought or emotion, ELIZA sometimes provided a startlingly human-like interaction. ![]() 1960s: Some notably successful natural language processing systems developed in the 1960s were SHRDLU, a natural language system working in restricted " blocks worlds" with restricted vocabularies, and ELIZA, a simulation of a Rogerian psychotherapist, written by Joseph Weizenbaum between 19.Little further research in machine translation was conducted in America (though some research continued elsewhere, such as Japan and Europe ) until the late 1980s when the first statistical machine translation systems were developed. However, real progress was much slower, and after the ALPAC report in 1966, which found that ten-year-long research had failed to fulfill the expectations, funding for machine translation was dramatically reduced. The authors claimed that within three or five years, machine translation would be a solved problem. 1950s: The Georgetown experiment in 1954 involved fully automatic translation of more than sixty Russian sentences into English.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. The proposed test includes a task that involves the automated interpretation and generation of natural language. Already in 1950, Alan Turing published an article titled " Computing Machinery and Intelligence" which proposed what is now called the Turing test as a criterion of intelligence, though at the time that was not articulated as a problem separate from artificial intelligence. Natural language processing has its roots in the 1950s. Further information: History of natural language processing
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