Show Summary Details

Page of

PRINTED FROM OXFORD HANDBOOKS ONLINE ( © Oxford University Press, 2018. All Rights Reserved. Under the terms of the licence agreement, an individual user may print out a PDF of a single chapter of a title in Oxford Handbooks Online for personal use (for details see Privacy Policy and Legal Notice).

date: 19 July 2018

(p. xvii) Introduction

(p. xvii) Introduction

Computational Linguistics is about as robust a field of intellectual endeavour as one could find, with its books, journals, conferences, professorial chairs, societies, associations and the like. But, of course, it was not always so. Computational Linguistics crept into existence shyly, almost furtively. When shall we say it all began? Perhaps in 1949, when Warren Weaver wrote his famous memorandum suggesting that translation by machine might be possible. The first conference on machine translation took place at MIT in 1952 and the first journal, Mechanical Translation, began in 1954. However, the phrase ‘Computational Linguistics’ started to appear only in the mid-1960s. The journal changed its name to Mechanical Translation and Computational Linguistics in 1965 but the words ‘and Computational Linguistics’ appeared in very small type. This change coincided with the adoption of the journal by the Association for Machine Translation and Computational Linguistics, which was formed in 1962.

The term ‘Computational Linguistics’ was probably coined by David Hays during the time that he was a member of the Automatic Language Processing Advisory Committee of the National Academy of Sciences. The publication of this committee's final report, generally known as the ALPAC report, certainly constituted one of the most dramatic moments in the history of the field - proposing, as it did, that machine translation be abandoned as a short-term engineering goal in favour of more fundamental scientific research in language and language processing. Hays saw this coming and realized that, if the money that had been flowing into machine translation could be diverted into a new field of enquiry, the most pressing requirement was for the field to be given a name. The name took hold. Redirection of the funds did not.

Progression from machine translation to Computational Linguistics occurred in 1974 when Machine Translation and Computational Linguistics was replaced by the American Journal of Computational Linguistics, which appeared initially only in microfiche form. In 1980, this became Computational Linguistics, which is still alive and vigorous today.

By the 1980s, machine translation began to look practical again, at least to some people and for some purposes and, in 1986, the circle was completed with the publication of the first issue of Computers and Translation, renamed Machine Translation in 1988. The International Journal of Machine Translation followed in 1991.

(p. xviii) Warren Weaver's vision of machine translation came from his war-time experience as a cryptographer and he considered the problem to be one of treating textual material, by fundamentally statistical techniques. But the founders of Computational Linguistics were mostly linguists, not statisticians, and they saw the potential of the computer less in the possibility of deriving a characterization of the translation relation from emergent properties of parallel corpora, than in carrying out exactly, and with great speed, the minutely specified rules that they would write. Chomsky's Syntactic Structures (1957) served to solidify the notion of grammar as a deductive system which therefore seemed eminently suited to computer applications. The fact that Chomsky himself saw little value in such an enterprise, or that the particular scheme of axioms and rules that he advocated was ill suited to the automatic analysis of text, did nothing to diminish the attractiveness of the general idea.

Computational Linguistics thus came to be an exercise in creating and implementing the formal systems that were increasingly seen as constituting the core of linguistic theory. If any single event marks the birth of the field, it is surely the proposal by John Cocke in 1960 of the scheme for deriving all analyses of a string with a grammar of binary context-free rules that we now know as the Cocke-Kasami-Younger algorithm. It soon became clear that more powerful formalisms would be required to meet the specific needs of human language, and more general chart parsers, augmented transition networks, unification grammars, and many other formal and computational devices were created.

There were two principal motivations for this activity. One was theoretical and came from the growing perception that the pursuit of computational goals could give rise to important advances in linguistic theory. Requiring that a formal system be implement able helped to ensure its internal consistency and revealed its formal complexity properties. The results are to be seen most clearly in syntactic formalisms such as Generalized Phrase Structure Grammar, Lexical Functional Grammar, and Head Driven Phrase Structure as well as in application of finite-state methods to phonology and morphology.

The second motivation, which had existed from the beginning, came from the desire to create a technology, based on sound scientific principles, to support a large and expanding list of practical requirements for translation, information extraction, summarization, grammar checking, and the like. In none of these enterprises is success achievable by linguistic methods alone. To varying extent, each involves language not just as a formal system, but as a means of encoding and conveying information about something outside, something which, for want of a better term, we may loosely call 'the world: Much of the robustness of language comes from the imprecision and ambiguity which allow people to use it in a casual manner. But this works only because people are able to restore missing information and resolve ambiguities on the basis of what makes sense in a larger context provided not only by the surrounding words but by the world outside. If there is any field that should be responsible for the construction of comprehensive, general models of the world, it (p. xix) is presumably artificial intelligence, but the task is clearly a great deal more daunting even than building comprehensive linguistic models, and success has been limited.

As a result, Computational Linguistics has gained a reputation for not measuring up to the challenges of technology, and this in turn has given rise to much frustration and misunderstanding both within and outside the community of computational linguists. There is, of course, much that still remains to be done by computational linguists, but very little of the responsibility for the apparently poor showing of the field belongs to them. As I have said, a significant reason for this is the lack of a broader technological environment in which Computational Linguistics can thrive. Lacking an artificial intelligence in which to embed their technology, linguists have been forced to seek a surrogate, however imperfect, and many think they have found it in what is generally known as ‘statistical natural language processing’.

Roughly speaking, statistical NLP associates probabilities with the alternatives encountered in the course of analysing an utterance or a text and accepts the most probable outcome as the correct one. In ‘the boy saw the girl with the telescope’, the phrase ‘with the telescope’ is more likely to modify ‘saw’ than ‘the girl’ let us say, because ‘telescope’ has often been observed in situations which, like this one, represent it as an instrument for seeing. This is an undeniable fact about seeing and telescopes, but it is not a fact about English. Not surprisingly, words that name phenomena that are closely related in the world, or our perception of it, frequently occur close to one another so that crisp facts about the world are reflected in somewhat fuzzier facts about texts.

There is much room for debate in this view. The more fundamentalist of its proponents claim that the only hope for constructing useful systems for processing natural language is to learn them entirely from primary data as children do. If the analogy is good, and if Chomsky is right, this implies that the systems must be strongly predisposed towards certain kinds of languages because the primary data provides no negative examples and the information that it contains occurs, in any case, in too weak dilution to support the construction of sufficiently robust models without strong initial constraints.

If, as I have suggested, text processing depends on knowledge of the world as well as knowledge of language, then the proponents of radical statistical NLP face a stronger challenge than Chomsky's language learner because they must also construct this knowledge of the world entirely on the basis of what they read about it, and in no way on the basis of direct experience. The question that remains wide open is: Just how much of the knowledge of these two kinds that is required for NLP is derivable, even in principle, from emergent properties of text? The work done over the next few years should do much to clarify the issue and thus to suggest the direction that the field will follow thereafter.

This book stands on its own in the sense that it will not only bring people working in the field up to date on what is going on in parallel specialities to their own, but also introduce outsiders to the aims, methods, and achievements of computational (p. xx) linguists. The chapters of Part I have the same titles that one might expect to find in an introductory text on general linguistics. With the exception of the last, they correspond to the various levels of abstraction on which linguists work, from individual sounds to structures that span whole texts or dialogues, to the interface between meaning and the objective world, and the making of dictionaries. The difference, of course, is that they concentrate on the opportunities for computational exploration that each of these domains opens up, and on the problems that must be solved in each of them before they can contribute to the creation of linguistic technology.

I have suggested that requiring a formal system to be implementable led linguists to attend to the formal complexity properties of their theories. The last chapter of Part I provides an introduction to the mathematical notion of complexity and explores the crucial role that it plays in Computational Linguistics.

part II of the book gives a chapter to each of the areas that have turned out to be the principal centres of activity in the field. For these purposes, Computational Linguistics is construed very broadly. On the one hand, it treats speech recognition and text-to-speech synthesis, the fundamentals of which are more often studied in departments of electrical engineering than linguistics and on the other, it contains a chapter entitled ‘Corpora’ an activity in which students of language use large collections of text or recorded speech as sources of evidence in their investigations. Part III is devoted to plications-starting, as is only fitting, with a pair of chapters on machine translation followed by a discussion of some topics that are at the centre of attention in the field at the present.

It is clear from the table of contents alone that, during the half century in which the field, if not the name, of Computational Linguistics has existed, it has come to cover a very wide territory, enriching virtually every part of theoretical linguistics with a computational and a technological component. However, it has been only poorly supplied with textbooks or comprehensive reference works. This book should go a long way towards meeting the second need.