NLP is incredibly old. The use of the computer for calculating artillery tables and code-breaking was less pressing for a few years after 1945. Peace time gave researchers the opportunity to allow their imagination roam over new applications. Until about 1960, it was quite feasible to write definitive histories of NLP, with reviews of any and all significant work. However, since then, there has been so much research, that it is no longer reasonable to write exhaustive histories. However, it is possible to pick out the most influential systems and the trends that emerged, and this is what this section does.
As a research idea, this caught on quickly, with significant machine translation research groups set up in the USA, UK, France and the Soviet Union. Early American systems concentrated on the translation of German to English, because there were technical documents left over from the war to be translated. As time passed and the German material was seen as out-dated, the pairs of languages in the research projects moved to Russian to English, Russian to French or English to Russian and French to Russian. The Cold War had caught up with machine translation research.
Early machine translation systems were conspicuously unsuccessful. Even worse, they eventually brought the hostility of research funding agencies down upon the heads of NLP workers. Warren Weaver's memo of 1949 had inspired many projects, all of which were breaking new ground: there was no received wisdom in NLP, no body of knowledge and techniques to be applied. The early workers were often mathematicians who struggled with the primitive computing machinery. Some early workers were bilingual, eg native German speakers who had emigrated to the USA. Their knowledge of both languages in their system suggested that they would be able to write programs that would satisfactorily translate technical texts, at least. It soon became apparent that the task they had set themselves was extremely difficult. Language was far more complex than they had imagined. Worse still, although they were fluent speakers of their native language, it proved very difficult to encode their knowledge of the language in a computer program.
The obvious place to look for help was from Linguistics. The literature of the 1950s shows a growing awareness of work in mainstream Linguistics, and it became something of a trend for young researchers in Linguistics to join Machine Translation teams. While an openness to the contribution of related disciplines was to be welcomed, it is unclear that it helped Machine Translation a great deal, because there just were not suitable linguistic theories in existence. This changed in 1957 with the publication of Syntactic Structures by the young American Linguist who has dominated theoretical linguistics ever since, Noam Chomsky. Chomsky has revolutionised linguistics, perhaps almost single-handed. He introduced the idea of Generative Grammar: rule-based descriptions of syntactic structures. Although many have disagreed with Chomsky's ideas, producing alternative linguistic formalisms or taking issue with his methods of discovering linguistic data, almost all work in NLP since 1957 has been marked by his influence.
Early machine translation researchers realised that their systems could not translate the input texts without further assistance. Given the paucity of Linguistic theories, especially before 1957, some people proposed that texts should be pre-edited so as to mark difficulties in the text, for instance to disambiguate ambiguous words (for instance, in English, "ball"). As Machine Translation systems couldn't produce fluent output, the "target" language would have to be edited to polish it into a comprehensible text.
The introduction of pre and post-editing of machine translated texts had introduced the idea that the computer could be used as a tool to assist the human in tasks which were still too difficult for the computer to achieve on its own. In assisted machine translation, the computer acts as the memory, relieving the human of the need to know vast amount of vocabulary. Bar-Hillel reviewed the field and concluded that Fully-Automatic High-Quality Translation (FAHQT) is impossible without knowledge. He reviewed the then current projects and concluded that the methods they used, which in essence shuffled pairs of words, were inherently doomed to fail, even if extended significantly. The reason was simple: human translators add their understanding of the document to be translated to their knowledge of the structures of the languages they are working with. There remain some constructions that just require an understanding of the document or the way the world is for them to be correctly translated. In a language like English, it is difficult to know what the speaker of a sentence like:
"She wore small shoes and socks."
intended. (Were the socks also small?) For many purposes it doesn't matter, but if the system were analysing witness statements to initiate a search, it could be crucial.
Bar-Hillel's comments have had a long-lasting influence on the perception of the practicality of NLP and Machine Translation in particular. The other damning factor was the over-selling of systems. Research projects have to secure long-term funding in order to keep research teams together. In a situation where there are many teams working on the same basic area, it is crucial to be seen to be making good progress. Sponsors like to see clear practical demonstrations of the results of their funding. Machine Translation suffered from over selling itself up until the mid-1960s. This was not helped by the willingness of some of the press to put an (perhaps naive) optimistic gloss on any development. For Machine Translation, the demonstration of the Georgetown system on 7th January 1955 was just such an occasion. Looking back on this system with the hindsight of forty years, it seems an incredibly crude system that never had a hope of translating any by the most carefully chosen texts. At the time, it was greeted as the advent of practical Machine Translation.
US funding of Machine Translation research was reckoned to have cost the public purse $20 million by the mid 1960s. The Automatic Language Processing Advisory Committee (ALPAC) produced a report on the results of the funding and concluded that "there had been no machine translation of general scientific text, and none is in immediate prospect". US funding for machine translation was stopped, and this had the effect of halting most associated work in non-Machine Translation NLP. This had the knock-on effect of halting funding in other countries and NLP entered something of a dormant phase.
The key developments were:
John bought a ticket for Mary in the Symphony Hall Booking Office.
We know from the position of the words John and ticket that John is the agent instigating the action and that the ticket is the patient (or object) of the action. We know that Mary is the beneficiary of the action because of the use of the preposition for before her name. (What would have been the meaning of the sentence if that preposition had been from?) The location of the action was the Symphony Hall Booking Office, as is indicated by the use of the preposition in.
Charles Fillmore noticed that some languages donÕt have prepositions, but can still encode the same kinds of meaning. An analysis showed that they used different methods to express this information, for instance, the use of differing word endings (grammatical case) to indicate the role that a noun was playing in relation to a verb. (In English, we have the remnant of this method in the possessive: "John's book", where the ending of John is changed to show the role its playing in the sentence.) Other languages used rigid word order. Fillmore proposed that there are a very small number of "deep cases" which represent the possible relations between a verb and its nouns. Individual languages express this deep cases in a variety of ways, such as word order, prepositions, word inflection (ie changing the endings of words).
The significance of the proposal for NLP is that it contributed a relatively easily implementable theory which could contribute much semantic information with little processing effort. It also contributed to the solution of one of the intractable problems of Machine Translation: the translation of prepositions.
The key systems were: