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1 semantic system
Большой англо-русский и русско-английский словарь > semantic system
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2 semantic system
Математика: семантическая система -
3 semantic system
znaÄenjski sustav -
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1) система; комплекс2) совокупность•- absolutely consistent system - absolutely direct indecomposable system - absolutely free system - absolutely irreducible system - absolutely isolated system - allowable coordinate system - almost linear system - ample linear system - artificial feel system - automatic block system - automatic deicing system - binary relational system - binary-coded decimal system - block tooling system - Cartesian coordinate system - completely controllable system - completely ergodic system - completely hyperbolic system - completely identifiable system - completely integrable system - completely irreducible system - completely regular system - completely stable system - completely stratified system - complex number system - conical coordinate system - derivational formal system - differential equation system - differential selsyn system - digital counting system - digital transmission system - elliptic coordinate system - elliptic cylindrical coordinate system - externally inconsistent system - finite state system - finitely axiomatizable system - finitely presented system - fully characteristic quotient system - fundamental system of solutions - hydraulic lift system - integrated switching system - isomorphically embedded system - kernel normal system - linearly dependent system - linearly independent system - live hydraulic system - locking protection system - meteor-burst communication system - modular programming system - parabolic cylindrical coordinate system - permanent four-wheel drive system - pure independent system - radio telephone system - reactor protection system - real number system - receiver-amplifier crioelectric system - remote-cylinder hydraulic system - semantically consistent system - simply consistent system - simply incomplete system - simply ordered system - spherical coordinate system - strongly multiplicative system - structurally stable system - sufficiently general coordinate system - system of frequency curves - system of rational numbers - time multiplex system - time-division multiplex system - uniformly complete system - univalent system of notation - universal system of notation - weakly closed system - weighted number system -
6 семантическая система
Большой англо-русский и русско-английский словарь > семантическая система
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7 Artificial Intelligence
In my opinion, none of [these programs] does even remote justice to the complexity of human mental processes. Unlike men, "artificially intelligent" programs tend to be single minded, undistractable, and unemotional. (Neisser, 1967, p. 9)Future progress in [artificial intelligence] will depend on the development of both practical and theoretical knowledge.... As regards theoretical knowledge, some have sought a unified theory of artificial intelligence. My view is that artificial intelligence is (or soon will be) an engineering discipline since its primary goal is to build things. (Nilsson, 1971, pp. vii-viii)Most workers in AI [artificial intelligence] research and in related fields confess to a pronounced feeling of disappointment in what has been achieved in the last 25 years. Workers entered the field around 1950, and even around 1960, with high hopes that are very far from being realized in 1972. In no part of the field have the discoveries made so far produced the major impact that was then promised.... In the meantime, claims and predictions regarding the potential results of AI research had been publicized which went even farther than the expectations of the majority of workers in the field, whose embarrassments have been added to by the lamentable failure of such inflated predictions....When able and respected scientists write in letters to the present author that AI, the major goal of computing science, represents "another step in the general process of evolution"; that possibilities in the 1980s include an all-purpose intelligence on a human-scale knowledge base; that awe-inspiring possibilities suggest themselves based on machine intelligence exceeding human intelligence by the year 2000 [one has the right to be skeptical]. (Lighthill, 1972, p. 17)4) Just as Astronomy Succeeded Astrology, the Discovery of Intellectual Processes in Machines Should Lead to a Science, EventuallyJust as astronomy succeeded astrology, following Kepler's discovery of planetary regularities, the discoveries of these many principles in empirical explorations on intellectual processes in machines should lead to a science, eventually. (Minsky & Papert, 1973, p. 11)5) Problems in Machine Intelligence Arise Because Things Obvious to Any Person Are Not Represented in the ProgramMany problems arise in experiments on machine intelligence because things obvious to any person are not represented in any program. One can pull with a string, but one cannot push with one.... Simple facts like these caused serious problems when Charniak attempted to extend Bobrow's "Student" program to more realistic applications, and they have not been faced up to until now. (Minsky & Papert, 1973, p. 77)What do we mean by [a symbolic] "description"? We do not mean to suggest that our descriptions must be made of strings of ordinary language words (although they might be). The simplest kind of description is a structure in which some features of a situation are represented by single ("primitive") symbols, and relations between those features are represented by other symbols-or by other features of the way the description is put together. (Minsky & Papert, 1973, p. 11)[AI is] the use of computer programs and programming techniques to cast light on the principles of intelligence in general and human thought in particular. (Boden, 1977, p. 5)The word you look for and hardly ever see in the early AI literature is the word knowledge. They didn't believe you have to know anything, you could always rework it all.... In fact 1967 is the turning point in my mind when there was enough feeling that the old ideas of general principles had to go.... I came up with an argument for what I called the primacy of expertise, and at the time I called the other guys the generalists. (Moses, quoted in McCorduck, 1979, pp. 228-229)9) Artificial Intelligence Is Psychology in a Particularly Pure and Abstract FormThe basic idea of cognitive science is that intelligent beings are semantic engines-in other words, automatic formal systems with interpretations under which they consistently make sense. We can now see why this includes psychology and artificial intelligence on a more or less equal footing: people and intelligent computers (if and when there are any) turn out to be merely different manifestations of the same underlying phenomenon. Moreover, with universal hardware, any semantic engine can in principle be formally imitated by a computer if only the right program can be found. And that will guarantee semantic imitation as well, since (given the appropriate formal behavior) the semantics is "taking care of itself" anyway. Thus we also see why, from this perspective, artificial intelligence can be regarded as psychology in a particularly pure and abstract form. The same fundamental structures are under investigation, but in AI, all the relevant parameters are under direct experimental control (in the programming), without any messy physiology or ethics to get in the way. (Haugeland, 1981b, p. 31)There are many different kinds of reasoning one might imagine:Formal reasoning involves the syntactic manipulation of data structures to deduce new ones following prespecified rules of inference. Mathematical logic is the archetypical formal representation. Procedural reasoning uses simulation to answer questions and solve problems. When we use a program to answer What is the sum of 3 and 4? it uses, or "runs," a procedural model of arithmetic. Reasoning by analogy seems to be a very natural mode of thought for humans but, so far, difficult to accomplish in AI programs. The idea is that when you ask the question Can robins fly? the system might reason that "robins are like sparrows, and I know that sparrows can fly, so robins probably can fly."Generalization and abstraction are also natural reasoning process for humans that are difficult to pin down well enough to implement in a program. If one knows that Robins have wings, that Sparrows have wings, and that Blue jays have wings, eventually one will believe that All birds have wings. This capability may be at the core of most human learning, but it has not yet become a useful technique in AI.... Meta- level reasoning is demonstrated by the way one answers the question What is Paul Newman's telephone number? You might reason that "if I knew Paul Newman's number, I would know that I knew it, because it is a notable fact." This involves using "knowledge about what you know," in particular, about the extent of your knowledge and about the importance of certain facts. Recent research in psychology and AI indicates that meta-level reasoning may play a central role in human cognitive processing. (Barr & Feigenbaum, 1981, pp. 146-147)Suffice it to say that programs already exist that can do things-or, at the very least, appear to be beginning to do things-which ill-informed critics have asserted a priori to be impossible. Examples include: perceiving in a holistic as opposed to an atomistic way; using language creatively; translating sensibly from one language to another by way of a language-neutral semantic representation; planning acts in a broad and sketchy fashion, the details being decided only in execution; distinguishing between different species of emotional reaction according to the psychological context of the subject. (Boden, 1981, p. 33)Can the synthesis of Man and Machine ever be stable, or will the purely organic component become such a hindrance that it has to be discarded? If this eventually happens-and I have... good reasons for thinking that it must-we have nothing to regret and certainly nothing to fear. (Clarke, 1984, p. 243)The thesis of GOFAI... is not that the processes underlying intelligence can be described symbolically... but that they are symbolic. (Haugeland, 1985, p. 113)14) Artificial Intelligence Provides a Useful Approach to Psychological and Psychiatric Theory FormationIt is all very well formulating psychological and psychiatric theories verbally but, when using natural language (even technical jargon), it is difficult to recognise when a theory is complete; oversights are all too easily made, gaps too readily left. This is a point which is generally recognised to be true and it is for precisely this reason that the behavioural sciences attempt to follow the natural sciences in using "classical" mathematics as a more rigorous descriptive language. However, it is an unfortunate fact that, with a few notable exceptions, there has been a marked lack of success in this application. It is my belief that a different approach-a different mathematics-is needed, and that AI provides just this approach. (Hand, quoted in Hand, 1985, pp. 6-7)We might distinguish among four kinds of AI.Research of this kind involves building and programming computers to perform tasks which, to paraphrase Marvin Minsky, would require intelligence if they were done by us. Researchers in nonpsychological AI make no claims whatsoever about the psychological realism of their programs or the devices they build, that is, about whether or not computers perform tasks as humans do.Research here is guided by the view that the computer is a useful tool in the study of mind. In particular, we can write computer programs or build devices that simulate alleged psychological processes in humans and then test our predictions about how the alleged processes work. We can weave these programs and devices together with other programs and devices that simulate different alleged mental processes and thereby test the degree to which the AI system as a whole simulates human mentality. According to weak psychological AI, working with computer models is a way of refining and testing hypotheses about processes that are allegedly realized in human minds.... According to this view, our minds are computers and therefore can be duplicated by other computers. Sherry Turkle writes that the "real ambition is of mythic proportions, making a general purpose intelligence, a mind." (Turkle, 1984, p. 240) The authors of a major text announce that "the ultimate goal of AI research is to build a person or, more humbly, an animal." (Charniak & McDermott, 1985, p. 7)Research in this field, like strong psychological AI, takes seriously the functionalist view that mentality can be realized in many different types of physical devices. Suprapsychological AI, however, accuses strong psychological AI of being chauvinisticof being only interested in human intelligence! Suprapsychological AI claims to be interested in all the conceivable ways intelligence can be realized. (Flanagan, 1991, pp. 241-242)16) Determination of Relevance of Rules in Particular ContextsEven if the [rules] were stored in a context-free form the computer still couldn't use them. To do that the computer requires rules enabling it to draw on just those [ rules] which are relevant in each particular context. Determination of relevance will have to be based on further facts and rules, but the question will again arise as to which facts and rules are relevant for making each particular determination. One could always invoke further facts and rules to answer this question, but of course these must be only the relevant ones. And so it goes. It seems that AI workers will never be able to get started here unless they can settle the problem of relevance beforehand by cataloguing types of context and listing just those facts which are relevant in each. (Dreyfus & Dreyfus, 1986, p. 80)Perhaps the single most important idea to artificial intelligence is that there is no fundamental difference between form and content, that meaning can be captured in a set of symbols such as a semantic net. (G. Johnson, 1986, p. 250)Artificial intelligence is based on the assumption that the mind can be described as some kind of formal system manipulating symbols that stand for things in the world. Thus it doesn't matter what the brain is made of, or what it uses for tokens in the great game of thinking. Using an equivalent set of tokens and rules, we can do thinking with a digital computer, just as we can play chess using cups, salt and pepper shakers, knives, forks, and spoons. Using the right software, one system (the mind) can be mapped into the other (the computer). (G. Johnson, 1986, p. 250)19) A Statement of the Primary and Secondary Purposes of Artificial IntelligenceThe primary goal of Artificial Intelligence is to make machines smarter.The secondary goals of Artificial Intelligence are to understand what intelligence is (the Nobel laureate purpose) and to make machines more useful (the entrepreneurial purpose). (Winston, 1987, p. 1)The theoretical ideas of older branches of engineering are captured in the language of mathematics. We contend that mathematical logic provides the basis for theory in AI. Although many computer scientists already count logic as fundamental to computer science in general, we put forward an even stronger form of the logic-is-important argument....AI deals mainly with the problem of representing and using declarative (as opposed to procedural) knowledge. Declarative knowledge is the kind that is expressed as sentences, and AI needs a language in which to state these sentences. Because the languages in which this knowledge usually is originally captured (natural languages such as English) are not suitable for computer representations, some other language with the appropriate properties must be used. It turns out, we think, that the appropriate properties include at least those that have been uppermost in the minds of logicians in their development of logical languages such as the predicate calculus. Thus, we think that any language for expressing knowledge in AI systems must be at least as expressive as the first-order predicate calculus. (Genesereth & Nilsson, 1987, p. viii)21) Perceptual Structures Can Be Represented as Lists of Elementary PropositionsIn artificial intelligence studies, perceptual structures are represented as assemblages of description lists, the elementary components of which are propositions asserting that certain relations hold among elements. (Chase & Simon, 1988, p. 490)Artificial intelligence (AI) is sometimes defined as the study of how to build and/or program computers to enable them to do the sorts of things that minds can do. Some of these things are commonly regarded as requiring intelligence: offering a medical diagnosis and/or prescription, giving legal or scientific advice, proving theorems in logic or mathematics. Others are not, because they can be done by all normal adults irrespective of educational background (and sometimes by non-human animals too), and typically involve no conscious control: seeing things in sunlight and shadows, finding a path through cluttered terrain, fitting pegs into holes, speaking one's own native tongue, and using one's common sense. Because it covers AI research dealing with both these classes of mental capacity, this definition is preferable to one describing AI as making computers do "things that would require intelligence if done by people." However, it presupposes that computers could do what minds can do, that they might really diagnose, advise, infer, and understand. One could avoid this problematic assumption (and also side-step questions about whether computers do things in the same way as we do) by defining AI instead as "the development of computers whose observable performance has features which in humans we would attribute to mental processes." This bland characterization would be acceptable to some AI workers, especially amongst those focusing on the production of technological tools for commercial purposes. But many others would favour a more controversial definition, seeing AI as the science of intelligence in general-or, more accurately, as the intellectual core of cognitive science. As such, its goal is to provide a systematic theory that can explain (and perhaps enable us to replicate) both the general categories of intentionality and the diverse psychological capacities grounded in them. (Boden, 1990b, pp. 1-2)Because the ability to store data somewhat corresponds to what we call memory in human beings, and because the ability to follow logical procedures somewhat corresponds to what we call reasoning in human beings, many members of the cult have concluded that what computers do somewhat corresponds to what we call thinking. It is no great difficulty to persuade the general public of that conclusion since computers process data very fast in small spaces well below the level of visibility; they do not look like other machines when they are at work. They seem to be running along as smoothly and silently as the brain does when it remembers and reasons and thinks. On the other hand, those who design and build computers know exactly how the machines are working down in the hidden depths of their semiconductors. Computers can be taken apart, scrutinized, and put back together. Their activities can be tracked, analyzed, measured, and thus clearly understood-which is far from possible with the brain. This gives rise to the tempting assumption on the part of the builders and designers that computers can tell us something about brains, indeed, that the computer can serve as a model of the mind, which then comes to be seen as some manner of information processing machine, and possibly not as good at the job as the machine. (Roszak, 1994, pp. xiv-xv)The inner workings of the human mind are far more intricate than the most complicated systems of modern technology. Researchers in the field of artificial intelligence have been attempting to develop programs that will enable computers to display intelligent behavior. Although this field has been an active one for more than thirty-five years and has had many notable successes, AI researchers still do not know how to create a program that matches human intelligence. No existing program can recall facts, solve problems, reason, learn, and process language with human facility. This lack of success has occurred not because computers are inferior to human brains but rather because we do not yet know in sufficient detail how intelligence is organized in the brain. (Anderson, 1995, p. 2)Historical dictionary of quotations in cognitive science > Artificial Intelligence
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8 Memory
To what extent can we lump together what goes on when you try to recall: (1) your name; (2) how you kick a football; and (3) the present location of your car keys? If we use introspective evidence as a guide, the first seems an immediate automatic response. The second may require constructive internal replay prior to our being able to produce a verbal description. The third... quite likely involves complex operational responses under the control of some general strategy system. Is any unitary search process, with a single set of characteristics and inputoutput relations, likely to cover all these cases? (Reitman, 1970, p. 485)[Semantic memory] Is a mental thesaurus, organized knowledge a person possesses about words and other verbal symbols, their meanings and referents, about relations among them, and about rules, formulas, and algorithms for the manipulation of these symbols, concepts, and relations. Semantic memory does not register perceptible properties of inputs, but rather cognitive referents of input signals. (Tulving, 1972, p. 386)The mnemonic code, far from being fixed and unchangeable, is structured and restructured along with general development. Such a restructuring of the code takes place in close dependence on the schemes of intelligence. The clearest indication of this is the observation of different types of memory organisation in accordance with the age level of a child so that a longer interval of retention without any new presentation, far from causing a deterioration of memory, may actually improve it. (Piaget & Inhelder, 1973, p. 36)4) The Logic of Some Memory Theorization Is of Dubious Worth in the History of PsychologyIf a cue was effective in memory retrieval, then one could infer it was encoded; if a cue was not effective, then it was not encoded. The logic of this theorization is "heads I win, tails you lose" and is of dubious worth in the history of psychology. We might ask how long scientists will puzzle over questions with no answers. (Solso, 1974, p. 28)We have iconic, echoic, active, working, acoustic, articulatory, primary, secondary, episodic, semantic, short-term, intermediate-term, and longterm memories, and these memories contain tags, traces, images, attributes, markers, concepts, cognitive maps, natural-language mediators, kernel sentences, relational rules, nodes, associations, propositions, higher-order memory units, and features. (Eysenck, 1977, p. 4)The problem with the memory metaphor is that storage and retrieval of traces only deals [ sic] with old, previously articulated information. Memory traces can perhaps provide a basis for dealing with the "sameness" of the present experience with previous experiences, but the memory metaphor has no mechanisms for dealing with novel information. (Bransford, McCarrell, Franks & Nitsch, 1977, p. 434)7) The Results of a Hundred Years of the Psychological Study of Memory Are Somewhat DiscouragingThe results of a hundred years of the psychological study of memory are somewhat discouraging. We have established firm empirical generalisations, but most of them are so obvious that every ten-year-old knows them anyway. We have made discoveries, but they are only marginally about memory; in many cases we don't know what to do with them, and wear them out with endless experimental variations. We have an intellectually impressive group of theories, but history offers little confidence that they will provide any meaningful insight into natural behavior. (Neisser, 1978, pp. 12-13)A schema, then is a data structure for representing the generic concepts stored in memory. There are schemata representing our knowledge about all concepts; those underlying objects, situations, events, sequences of events, actions and sequences of actions. A schema contains, as part of its specification, the network of interrelations that is believed to normally hold among the constituents of the concept in question. A schema theory embodies a prototype theory of meaning. That is, inasmuch as a schema underlying a concept stored in memory corresponds to the mean ing of that concept, meanings are encoded in terms of the typical or normal situations or events that instantiate that concept. (Rumelhart, 1980, p. 34)Memory appears to be constrained by a structure, a "syntax," perhaps at quite a low level, but it is free to be variable, deviant, even erratic at a higher level....Like the information system of language, memory can be explained in part by the abstract rules which underlie it, but only in part. The rules provide a basic competence, but they do not fully determine performance. (Campbell, 1982, pp. 228, 229)When people think about the mind, they often liken it to a physical space, with memories and ideas as objects contained within that space. Thus, we speak of ideas being in the dark corners or dim recesses of our minds, and of holding ideas in mind. Ideas may be in the front or back of our minds, or they may be difficult to grasp. With respect to the processes involved in memory, we talk about storing memories, of searching or looking for lost memories, and sometimes of finding them. An examination of common parlance, therefore, suggests that there is general adherence to what might be called the spatial metaphor. The basic assumptions of this metaphor are that memories are treated as objects stored in specific locations within the mind, and the retrieval process involves a search through the mind in order to find specific memories....However, while the spatial metaphor has shown extraordinary longevity, there have been some interesting changes over time in the precise form of analogy used. In particular, technological advances have influenced theoretical conceptualisations.... The original Greek analogies were based on wax tablets and aviaries; these were superseded by analogies involving switchboards, gramophones, tape recorders, libraries, conveyor belts, and underground maps. Most recently, the workings of human memory have been compared to computer functioning... and it has been suggested that the various memory stores found in computers have their counterparts in the human memory system. (Eysenck, 1984, pp. 79-80)Primary memory [as proposed by William James] relates to information that remains in consciousness after it has been perceived, and thus forms part of the psychological present, whereas secondary memory contains information about events that have left consciousness, and are therefore part of the psychological past. (Eysenck, 1984, p. 86)Once psychologists began to study long-term memory per se, they realized it may be divided into two main categories.... Semantic memories have to do with our general knowledge about the working of the world. We know what cars do, what stoves do, what the laws of gravity are, and so on. Episodic memories are largely events that took place at a time and place in our personal history. Remembering specific events about our own actions, about our family, and about our individual past falls into this category. With amnesia or in aging, what dims... is our personal episodic memories, save for those that are especially dear or painful to us. Our knowledge of how the world works remains pretty much intact. (Gazzaniga, 1988, p. 42)The nature of memory... provides a natural starting point for an analysis of thinking. Memory is the repository of many of the beliefs and representations that enter into thinking, and the retrievability of these representations can limit the quality of our thought. (Smith, 1990, p. 1)Historical dictionary of quotations in cognitive science > Memory
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9 Bibliography
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Cambridge: Cambridge University Press.Historical dictionary of quotations in cognitive science > Bibliography
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10 Language
Philosophy is written in that great book, the universe, which is always open, right before our eyes. But one cannot understand this book without first learning to understand the language and to know the characters in which it is written. It is written in the language of mathematics, and the characters are triangles, circles, and other figures. Without these, one cannot understand a single word of it, and just wanders in a dark labyrinth. (Galileo, 1990, p. 232)It never happens that it [a nonhuman animal] arranges its speech in various ways in order to reply appropriately to everything that may be said in its presence, as even the lowest type of man can do. (Descartes, 1970a, p. 116)It is a very remarkable fact that there are none so depraved and stupid, without even excepting idiots, that they cannot arrange different words together, forming of them a statement by which they make known their thoughts; while, on the other hand, there is no other animal, however perfect and fortunately circumstanced it may be, which can do the same. (Descartes, 1967, p. 116)Human beings do not live in the object world alone, nor alone in the world of social activity as ordinarily understood, but are very much at the mercy of the particular language which has become the medium of expression for their society. It is quite an illusion to imagine that one adjusts to reality essentially without the use of language and that language is merely an incidental means of solving specific problems of communication or reflection. The fact of the matter is that the "real world" is to a large extent unconsciously built on the language habits of the group.... We see and hear and otherwise experience very largely as we do because the language habits of our community predispose certain choices of interpretation. (Sapir, 1921, p. 75)It powerfully conditions all our thinking about social problems and processes.... No two languages are ever sufficiently similar to be considered as representing the same social reality. The worlds in which different societies live are distinct worlds, not merely the same worlds with different labels attached. (Sapir, 1985, p. 162)[A list of language games, not meant to be exhaustive:]Giving orders, and obeying them- Describing the appearance of an object, or giving its measurements- Constructing an object from a description (a drawing)Reporting an eventSpeculating about an eventForming and testing a hypothesisPresenting the results of an experiment in tables and diagramsMaking up a story; and reading itPlay actingSinging catchesGuessing riddlesMaking a joke; and telling itSolving a problem in practical arithmeticTranslating from one language into anotherLANGUAGE Asking, thanking, cursing, greeting, and praying-. (Wittgenstein, 1953, Pt. I, No. 23, pp. 11 e-12 e)We dissect nature along lines laid down by our native languages.... The world is presented in a kaleidoscopic flux of impressions which has to be organized by our minds-and this means largely by the linguistic systems in our minds.... No individual is free to describe nature with absolute impartiality but is constrained to certain modes of interpretation even while he thinks himself most free. (Whorf, 1956, pp. 153, 213-214)We dissect nature along the lines laid down by our native languages.The categories and types that we isolate from the world of phenomena we do not find there because they stare every observer in the face; on the contrary, the world is presented in a kaleidoscopic flux of impressions which has to be organized by our minds-and this means largely by the linguistic systems in our minds.... We are thus introduced to a new principle of relativity, which holds that all observers are not led by the same physical evidence to the same picture of the universe, unless their linguistic backgrounds are similar or can in some way be calibrated. (Whorf, 1956, pp. 213-214)9) The Forms of a Person's Thoughts Are Controlled by Unperceived Patterns of His Own LanguageThe forms of a person's thoughts are controlled by inexorable laws of pattern of which he is unconscious. These patterns are the unperceived intricate systematizations of his own language-shown readily enough by a candid comparison and contrast with other languages, especially those of a different linguistic family. (Whorf, 1956, p. 252)It has come to be commonly held that many utterances which look like statements are either not intended at all, or only intended in part, to record or impart straightforward information about the facts.... Many traditional philosophical perplexities have arisen through a mistake-the mistake of taking as straightforward statements of fact utterances which are either (in interesting non-grammatical ways) nonsensical or else intended as something quite different. (Austin, 1962, pp. 2-3)In general, one might define a complex of semantic components connected by logical constants as a concept. The dictionary of a language is then a system of concepts in which a phonological form and certain syntactic and morphological characteristics are assigned to each concept. This system of concepts is structured by several types of relations. It is supplemented, furthermore, by redundancy or implicational rules..., representing general properties of the whole system of concepts.... At least a relevant part of these general rules is not bound to particular languages, but represents presumably universal structures of natural languages. They are not learned, but are rather a part of the human ability to acquire an arbitrary natural language. (Bierwisch, 1970, pp. 171-172)In studying the evolution of mind, we cannot guess to what extent there are physically possible alternatives to, say, transformational generative grammar, for an organism meeting certain other physical conditions characteristic of humans. Conceivably, there are none-or very few-in which case talk about evolution of the language capacity is beside the point. (Chomsky, 1972, p. 98)[It is] truth value rather than syntactic well-formedness that chiefly governs explicit verbal reinforcement by parents-which renders mildly paradoxical the fact that the usual product of such a training schedule is an adult whose speech is highly grammatical but not notably truthful. (R. O. Brown, 1973, p. 330)he conceptual base is responsible for formally representing the concepts underlying an utterance.... A given word in a language may or may not have one or more concepts underlying it.... On the sentential level, the utterances of a given language are encoded within a syntactic structure of that language. The basic construction of the sentential level is the sentence.The next highest level... is the conceptual level. We call the basic construction of this level the conceptualization. A conceptualization consists of concepts and certain relations among those concepts. We can consider that both levels exist at the same point in time and that for any unit on one level, some corresponding realizate exists on the other level. This realizate may be null or extremely complex.... Conceptualizations may relate to other conceptualizations by nesting or other specified relationships. (Schank, 1973, pp. 191-192)The mathematics of multi-dimensional interactive spaces and lattices, the projection of "computer behavior" on to possible models of cerebral functions, the theoretical and mechanical investigation of artificial intelligence, are producing a stream of sophisticated, often suggestive ideas.But it is, I believe, fair to say that nothing put forward until now in either theoretic design or mechanical mimicry comes even remotely in reach of the most rudimentary linguistic realities. (Steiner, 1975, p. 284)The step from the simple tool to the master tool, a tool to make tools (what we would now call a machine tool), seems to me indeed to parallel the final step to human language, which I call reconstitution. It expresses in a practical and social context the same understanding of hierarchy, and shows the same analysis by function as a basis for synthesis. (Bronowski, 1977, pp. 127-128)t is the language donn eґ in which we conduct our lives.... We have no other. And the danger is that formal linguistic models, in their loosely argued analogy with the axiomatic structure of the mathematical sciences, may block perception.... It is quite conceivable that, in language, continuous induction from simple, elemental units to more complex, realistic forms is not justified. The extent and formal "undecidability" of context-and every linguistic particle above the level of the phoneme is context-bound-may make it impossible, except in the most abstract, meta-linguistic sense, to pass from "pro-verbs," "kernals," or "deep deep structures" to actual speech. (Steiner, 1975, pp. 111-113)A higher-level formal language is an abstract machine. (Weizenbaum, 1976, p. 113)Jakobson sees metaphor and metonymy as the characteristic modes of binarily opposed polarities which between them underpin the two-fold process of selection and combination by which linguistic signs are formed.... Thus messages are constructed, as Saussure said, by a combination of a "horizontal" movement, which combines words together, and a "vertical" movement, which selects the particular words from the available inventory or "inner storehouse" of the language. The combinative (or syntagmatic) process manifests itself in contiguity (one word being placed next to another) and its mode is metonymic. The selective (or associative) process manifests itself in similarity (one word or concept being "like" another) and its mode is metaphoric. The "opposition" of metaphor and metonymy therefore may be said to represent in effect the essence of the total opposition between the synchronic mode of language (its immediate, coexistent, "vertical" relationships) and its diachronic mode (its sequential, successive, lineal progressive relationships). (Hawkes, 1977, pp. 77-78)It is striking that the layered structure that man has given to language constantly reappears in his analyses of nature. (Bronowski, 1977, p. 121)First, [an ideal intertheoretic reduction] provides us with a set of rules"correspondence rules" or "bridge laws," as the standard vernacular has it-which effect a mapping of the terms of the old theory (T o) onto a subset of the expressions of the new or reducing theory (T n). These rules guide the application of those selected expressions of T n in the following way: we are free to make singular applications of their correspondencerule doppelgangers in T o....Second, and equally important, a successful reduction ideally has the outcome that, under the term mapping effected by the correspondence rules, the central principles of T o (those of semantic and systematic importance) are mapped onto general sentences of T n that are theorems of Tn. (P. Churchland, 1979, p. 81)If non-linguistic factors must be included in grammar: beliefs, attitudes, etc. [this would] amount to a rejection of the initial idealization of language as an object of study. A priori such a move cannot be ruled out, but it must be empirically motivated. If it proves to be correct, I would conclude that language is a chaos that is not worth studying.... Note that the question is not whether beliefs or attitudes, and so on, play a role in linguistic behavior and linguistic judgments... [but rather] whether distinct cognitive structures can be identified, which interact in the real use of language and linguistic judgments, the grammatical system being one of these. (Chomsky, 1979, pp. 140, 152-153)23) Language Is Inevitably Influenced by Specific Contexts of Human InteractionLanguage cannot be studied in isolation from the investigation of "rationality." It cannot afford to neglect our everyday assumptions concerning the total behavior of a reasonable person.... An integrational linguistics must recognize that human beings inhabit a communicational space which is not neatly compartmentalized into language and nonlanguage.... It renounces in advance the possibility of setting up systems of forms and meanings which will "account for" a central core of linguistic behavior irrespective of the situation and communicational purposes involved. (Harris, 1981, p. 165)By innate [linguistic knowledge], Chomsky simply means "genetically programmed." He does not literally think that children are born with language in their heads ready to be spoken. He merely claims that a "blueprint is there, which is brought into use when the child reaches a certain point in her general development. With the help of this blueprint, she analyzes the language she hears around her more readily than she would if she were totally unprepared for the strange gabbling sounds which emerge from human mouths. (Aitchison, 1987, p. 31)Looking at ourselves from the computer viewpoint, we cannot avoid seeing that natural language is our most important "programming language." This means that a vast portion of our knowledge and activity is, for us, best communicated and understood in our natural language.... One could say that natural language was our first great original artifact and, since, as we increasingly realize, languages are machines, so natural language, with our brains to run it, was our primal invention of the universal computer. One could say this except for the sneaking suspicion that language isn't something we invented but something we became, not something we constructed but something in which we created, and recreated, ourselves. (Leiber, 1991, p. 8)Historical dictionary of quotations in cognitive science > Language
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11 Grammar
I think that the failure to offer a precise account of the notion "grammar" is not just a superficial defect in linguistic theory that can be remedied by adding one more definition. It seems to me that until this notion is clarified, no part of linguistic theory can achieve anything like a satisfactory development.... I have been discussing a grammar of a particular language here as analogous to a particular scientific theory, dealing with its subject matter (the set of sentences of this language) much as embryology or physics deals with its subject matter. (Chomsky, 1964, p. 213)Obviously, every speaker of a language has mastered and internalized a generative grammar that expresses his knowledge of his language. This is not to say that he is aware of the rules of grammar or even that he can become aware of them, or that his statements about his intuitive knowledge of his language are necessarily accurate. (Chomsky, 1965, p. 8)Much effort has been devoted to showing that the class of possible transformations can be substantially reduced without loss of descriptive power through the discovery of quite general conditions that all such rules and the representations they operate on and form must meet.... [The] transformational rules, at least for a substantial core grammar, can be reduced to the single rule, "Move alpha" (that is, "move any category anywhere"). (Mehler, Walker & Garrett, 1982, p. 21)4) The Relationship of Transformational Grammar to Semantics and to Human Performancehe implications of assuming a semantic memory for what we might call "generative psycholinguistics" are: that dichotomous judgments of semantic well-formedness versus anomaly are not essential or inherent to language performance; that the transformational component of a grammar is the part most relevant to performance models; that a generative grammar's role should be viewed as restricted to language production, whereas sentence understanding should be treated as a problem of extracting a cognitive representation of a text's message; that until some theoretical notion of cognitive representation is incorporated into linguistic conceptions, they are unlikely to provide either powerful language-processing programs or psychologically relevant theories.Although these implications conflict with the way others have viewed the relationship of transformational grammars to semantics and to human performance, they do not eliminate the importance of such grammars to psychologists, an importance stressed in, and indeed largely created by, the work of Chomsky. It is precisely because of a growing interdependence between such linguistic theory and psychological performance models that their relationship needs to be clarified. (Quillian, 1968, p. 260)here are some terminological distinctions that are crucial to explain, or else confusions can easily arise. In the formal study of grammar, a language is defined as a set of sentences, possibly infinite, where each sentence is a string of symbols or words. One can think of each sentence as having several representations linked together: one for its sound pattern, one for its meaning, one for the string of words constituting it, possibly others for other data structures such as the "surface structure" and "deep structure" that are held to mediate the mapping between sound and meaning. Because no finite system can store an infinite number of sentences, and because humans in particular are clearly not pullstring dolls that emit sentences from a finite stored list, one must explain human language abilities by imputing to them a grammar, which in the technical sense is a finite rule system, or programme, or circuit design, capable of generating and recognizing the sentences of a particular language. This "mental grammar" or "psychogrammar" is the neural system that allows us to speak and understand the possible word sequences of our native tongue. A grammar for a specific language is obviously acquired by a human during childhood, but there must be neural circuitry that actually carries out the acquisition process in the child, and this circuitry may be called the language faculty or language acquisition device. An important part of the language faculty is universal grammar, an implementation of a set of principles or constraints that govern the possible form of any human grammar. (Pinker, 1996, p. 263)A grammar of language L is essentially a theory of L. Any scientific theory is based on a finite number of observations, and it seeks to relate the observed phenomena and to predict new phenomena by constructing general laws in terms of hypothetical constructs.... Similarly a grammar of English is based on a finite corpus of utterances (observations), and it will contain certain grammatical rules (laws) stated in terms of the particular phonemes, phrases, etc., of English (hypothetical constructs). These rules express structural relations among the sentences of the corpus and the infinite number of sentences generated by the grammar beyond the corpus (predictions). (Chomsky, 1957, p. 49)Historical dictionary of quotations in cognitive science > Grammar
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12 SID
1) Компьютерная техника: Searchable Image Database, Subunit Interaction Domain, System Installation Diskette, sudden ionospheric disturbances, System IDentifier, Silence Insertion Descriptor2) Авиация: стандартная схема выхода3) Медицина: Source-Image Distance4) Американизм: Scientific Investigation Division5) Спорт: Smad Interaction Domain6) Военный термин: Ship In Distress, Speed Indicator Device, Strategic Information Development, Subscriber Identification, security and intelligence division, seismic intrusion detector, situation display, special intelligence detachment, standard information display, strategic intelligence digest, supplemental inspection document, supplemental inspection document SICS, shipboard intelligence collection equipment, systems integration demonstrator, systems interface device, systems interface document, (S) Selective Imagery Dissemination (System)7) Техника: slew-induced distortion, sodium ionization detector, space and information systems division, synchronous identification system, system for interactive design, стандартный вылет по приборам8) Религия: Sermon And Illustration Database9) Метеорология: Sudden Ionosphere Disturbance10) Юридический термин: Sex In Doubt11) Грубое выражение: Sorry Inconsiderate Dick12) Оптика: Society for Information Display13) Телекоммуникации: System ID number14) Сокращение: Information Service, Ministry of National Defense (Belgium), Secondary Imagery Dissemination, Semantic IDentifier (computer programming), Ship Installation Drawings, Silicon Imaging Device, Situation Information Display console, Society for International Development, Source Image Distortion, Standard Instrument Departure, System Integration Development, seal-in device, special-purpose, spectral distribution, splashproof, spool, Subscriber Identification Number (мобильная телефония)15) Университет: Serious In Design, Sports Information Director, Support For Instructional Designers16) Физиология: Once per day17) Фото: Solubilization by Incipient Development18) Электроника: Slew Induced Distortion19) Вычислительная техника: Signaling IDentifier, System IDentification, security ID, segmented image database, сегментированная база данных изображений, SWIFT Interface Device (SWIFT), Sudden Ionospheric Disturbance (Space), Society for Information Displays (organization)20) Нефть: ionization smoke detector21) Дерматология: secondarily-infected dermatose22) Педиатрия: простая иммунодиффузия (single immunodiffusion)23) Транспорт: Side Impact Dummy, Speed Indicating Device, Static Impeller Driven24) Экология: sudden ionospheric disturbance25) Полицейский термин: Scientific Investigation Division, Отдел криминалистики полиции Лос-Анджелеса, Special Investigations Division26) СМИ: Sound Interface Device, Sports Illustrated Dog27) Деловая лексика: Super Intelligent Design28) Образование: Severe Intellectual Disability, идентификационный номер студента (Student Identification Number), номер личного дела студента29) Сетевые технологии: Shared Information Distance30) Криминология: state identification number31) Программирование: (Service Identification) идентификация сервиса (автоматизированное определение вида сервисных услуг, предоставляемых пользователям)32) Океанография: Shallow Intermediate And Deep34) Безопасность: Security In Depth, security identifier35) Расширение файла: Serial Input Data, Symbolic Interactive Debugger, Syntax Improving Device, System ID, Station Identification (AT&T)36) SAP.тех. идентификатор основных данных37) Майкрософт: ИД безопасности38) Должность: Seniority Integration Date39) Аэропорты: Sal, Cape Verde Islands40) НАСА: Spacecraft Identification Number41) Программное обеспечение: Software Item Description, Still In Development42) Базы данных: Simple Information Database -
13 Sid
1) Компьютерная техника: Searchable Image Database, Subunit Interaction Domain, System Installation Diskette, sudden ionospheric disturbances, System IDentifier, Silence Insertion Descriptor2) Авиация: стандартная схема выхода3) Медицина: Source-Image Distance4) Американизм: Scientific Investigation Division5) Спорт: Smad Interaction Domain6) Военный термин: Ship In Distress, Speed Indicator Device, Strategic Information Development, Subscriber Identification, security and intelligence division, seismic intrusion detector, situation display, special intelligence detachment, standard information display, strategic intelligence digest, supplemental inspection document, supplemental inspection document SICS, shipboard intelligence collection equipment, systems integration demonstrator, systems interface device, systems interface document, (S) Selective Imagery Dissemination (System)7) Техника: slew-induced distortion, sodium ionization detector, space and information systems division, synchronous identification system, system for interactive design, стандартный вылет по приборам8) Религия: Sermon And Illustration Database9) Метеорология: Sudden Ionosphere Disturbance10) Юридический термин: Sex In Doubt11) Грубое выражение: Sorry Inconsiderate Dick12) Оптика: Society for Information Display13) Телекоммуникации: System ID number14) Сокращение: Information Service, Ministry of National Defense (Belgium), Secondary Imagery Dissemination, Semantic IDentifier (computer programming), Ship Installation Drawings, Silicon Imaging Device, Situation Information Display console, Society for International Development, Source Image Distortion, Standard Instrument Departure, System Integration Development, seal-in device, special-purpose, spectral distribution, splashproof, spool, Subscriber Identification Number (мобильная телефония)15) Университет: Serious In Design, Sports Information Director, Support For Instructional Designers16) Физиология: Once per day17) Фото: Solubilization by Incipient Development18) Электроника: Slew Induced Distortion19) Вычислительная техника: Signaling IDentifier, System IDentification, security ID, segmented image database, сегментированная база данных изображений, SWIFT Interface Device (SWIFT), Sudden Ionospheric Disturbance (Space), Society for Information Displays (organization)20) Нефть: ionization smoke detector21) Дерматология: secondarily-infected dermatose22) Педиатрия: простая иммунодиффузия (single immunodiffusion)23) Транспорт: Side Impact Dummy, Speed Indicating Device, Static Impeller Driven24) Экология: sudden ionospheric disturbance25) Полицейский термин: Scientific Investigation Division, Отдел криминалистики полиции Лос-Анджелеса, Special Investigations Division26) СМИ: Sound Interface Device, Sports Illustrated Dog27) Деловая лексика: Super Intelligent Design28) Образование: Severe Intellectual Disability, идентификационный номер студента (Student Identification Number), номер личного дела студента29) Сетевые технологии: Shared Information Distance30) Криминология: state identification number31) Программирование: (Service Identification) идентификация сервиса (автоматизированное определение вида сервисных услуг, предоставляемых пользователям)32) Океанография: Shallow Intermediate And Deep34) Безопасность: Security In Depth, security identifier35) Расширение файла: Serial Input Data, Symbolic Interactive Debugger, Syntax Improving Device, System ID, Station Identification (AT&T)36) SAP.тех. идентификатор основных данных37) Майкрософт: ИД безопасности38) Должность: Seniority Integration Date39) Аэропорты: Sal, Cape Verde Islands40) НАСА: Spacecraft Identification Number41) Программное обеспечение: Software Item Description, Still In Development42) Базы данных: Simple Information Database -
14 sid
1) Компьютерная техника: Searchable Image Database, Subunit Interaction Domain, System Installation Diskette, sudden ionospheric disturbances, System IDentifier, Silence Insertion Descriptor2) Авиация: стандартная схема выхода3) Медицина: Source-Image Distance4) Американизм: Scientific Investigation Division5) Спорт: Smad Interaction Domain6) Военный термин: Ship In Distress, Speed Indicator Device, Strategic Information Development, Subscriber Identification, security and intelligence division, seismic intrusion detector, situation display, special intelligence detachment, standard information display, strategic intelligence digest, supplemental inspection document, supplemental inspection document SICS, shipboard intelligence collection equipment, systems integration demonstrator, systems interface device, systems interface document, (S) Selective Imagery Dissemination (System)7) Техника: slew-induced distortion, sodium ionization detector, space and information systems division, synchronous identification system, system for interactive design, стандартный вылет по приборам8) Религия: Sermon And Illustration Database9) Метеорология: Sudden Ionosphere Disturbance10) Юридический термин: Sex In Doubt11) Грубое выражение: Sorry Inconsiderate Dick12) Оптика: Society for Information Display13) Телекоммуникации: System ID number14) Сокращение: Information Service, Ministry of National Defense (Belgium), Secondary Imagery Dissemination, Semantic IDentifier (computer programming), Ship Installation Drawings, Silicon Imaging Device, Situation Information Display console, Society for International Development, Source Image Distortion, Standard Instrument Departure, System Integration Development, seal-in device, special-purpose, spectral distribution, splashproof, spool, Subscriber Identification Number (мобильная телефония)15) Университет: Serious In Design, Sports Information Director, Support For Instructional Designers16) Физиология: Once per day17) Фото: Solubilization by Incipient Development18) Электроника: Slew Induced Distortion19) Вычислительная техника: Signaling IDentifier, System IDentification, security ID, segmented image database, сегментированная база данных изображений, SWIFT Interface Device (SWIFT), Sudden Ionospheric Disturbance (Space), Society for Information Displays (organization)20) Нефть: ionization smoke detector21) Дерматология: secondarily-infected dermatose22) Педиатрия: простая иммунодиффузия (single immunodiffusion)23) Транспорт: Side Impact Dummy, Speed Indicating Device, Static Impeller Driven24) Экология: sudden ionospheric disturbance25) Полицейский термин: Scientific Investigation Division, Отдел криминалистики полиции Лос-Анджелеса, Special Investigations Division26) СМИ: Sound Interface Device, Sports Illustrated Dog27) Деловая лексика: Super Intelligent Design28) Образование: Severe Intellectual Disability, идентификационный номер студента (Student Identification Number), номер личного дела студента29) Сетевые технологии: Shared Information Distance30) Криминология: state identification number31) Программирование: (Service Identification) идентификация сервиса (автоматизированное определение вида сервисных услуг, предоставляемых пользователям)32) Океанография: Shallow Intermediate And Deep34) Безопасность: Security In Depth, security identifier35) Расширение файла: Serial Input Data, Symbolic Interactive Debugger, Syntax Improving Device, System ID, Station Identification (AT&T)36) SAP.тех. идентификатор основных данных37) Майкрософт: ИД безопасности38) Должность: Seniority Integration Date39) Аэропорты: Sal, Cape Verde Islands40) НАСА: Spacecraft Identification Number41) Программное обеспечение: Software Item Description, Still In Development42) Базы данных: Simple Information Database -
15 network
ˈnetwə:k сущ.
1) сеть, сетка;
плетенка
2) сеть а) (совокупность железных дорог, каналов и т. п.) communications network ≈ сеть коммуникаций, сеть связи, система связи road network ≈ сеть дорог network of rails ≈ сеть железных дорог community health network ≈ местная сеть здравоохранения (система медицинских учреждений, оказывающая помощь неимущим) б) взаимосвязанная цепочка или система нематериальных объектов;
схематическое представление событий и действий при изучении эффективности производственного процесса Network, a schematic representation of events and activities which shows their inter-relationships. ≈ Сеть - схематическое изображение событий и действий в их взаимосвязи. activity network ≈ сетевой график;
временная последовательность операций
3) тех. решетчатая система
4) радиотрансляционная сеть;
вещательная компания;
радиовещание, телевидение
5) электр. схема, цепь
6) взаимосвязанная группа людей, организация, сообщество I was paid about L500 for infiltrating the IRA network in London. ≈ Мне заплатили около 500 фунтов стерлингов за то, чтобы я проник в ряды ИРА в Лондоне. сеть, сетка плетенка сеточка, филе( в рукоделии) (текстильное) ажурное полотно плетение, вязание переплетение, хитросплетение;
сеть - * of intrigue сеть интриг - * of espionage шпионская сеть (электротехника) цепь, схема сеть (железнодорожная, канализационная, радиотрансляционная, телевизионная и т. п.) (техническое) решетчатая система (радиотехника) (телевидение) сетевое вещание - * announcer диктор (экономика) сетевой график( компьютерное) сеть - backbone * базовая сеть - ring * кольцевая сеть - * architecture архитектура сети ЭВМ - * data base сетевая база данных (американизм) покрывать сетью железных дорог - whole regions are *ed сетью железных дорог покрываются целые области( американизм) передавать, вещать по радио- или телевизионной сети( американизм) передаваться по радио- или телевизионной сети activity ~ сетевой график algorithmic ~ вчт. алгоритмическая сеть argumented transition ~ вчт. расширенная сеть переходов associative ~ вчт. ассоциативная сеть augmented transition ~ вчт. расширенная сеть переходов backbone ~ вчт. базовая сеть branch ~ сеть филиалов broadband ~ широкополосная сеть broadcast ~ широковещательная сеть causal ~ причинно-следственная сеть communications ~ сеть передачи данных communications ~ сеть связи computer ~ вычислительная сеть computer ~ компьютерная сеть computer ~ сеть вычислительных машин connectional ~ соединительная сеть data ~ вчт. сеть передачи данных dealer ~ торговая сеть distribution ~ распределительная сеть distribution ~ сеть распространения extensional semantic ~ семантическая экстенсиональная сеть hybrid ~ гибридная сеть inference ~ вчт. сеть вывода ladder ~ вчт. многозвенная схема local ~ вчт. локальная сеть network вычислительная сеть ~ радиотрансляционная сеть ~ тех. решетчатая система ~ сетевой график ~ сеть (железных дорог, каналов и т. п.) ~ сеть, сетка;
плетенка ~ вчт. сеть ~ сеть ~ сообщество ~ схема ~ эл. цепь, схема ~ data base вчт. сетевая база данных ~ file standard вчт. стандартный протокол сеевых файлов ~ file system вчт. сетевая файловая система ~ flow problem вчт. задача нахождения потока в сети ~ of queues вчт. многоканальная система обслуживания packet switching ~ вчт. сеть коммутации пакетов polled ~ вчт. сеть с опросом recursive transition ~ вчт. рекурсивная сеть переходов ring ~ вчт. кольцевая сеть road ~ дорожная сеть sales ~ сеть сбыта semantic ~ вчт. семантическая сеть slotted-ring ~ вчт. кольцевая сеть social ~ сеть социальных работников synchronous ~ вчт. синхронная сеть telecommunications ~ вчт. сеть связи telecommunications ~ система дальней связи telephone ~ телефонная сеть teleprinter ~ сеть телетайпной связи telex ~ система телексной связи terminal support ~ вчт. сеть поддержки терминалов traffic ~ транспортная сеть transition ~ вчт. сеть перехода состояний transport ~ вчт. транспортная сеть transport ~ транспортная сеть weighting ~ вчт. взвешивающая схема wideband ~ сеть связи с высокой пропускной способностью wideband ~ широкополосная сеть связи -
16 analysis
1) анализ; исследование2) расчёт•- analysis of behavior
- analysis of queues
- analysis of variance
- approximate analysis
- approximation analysis
- aspectological analysis
- automated analysis
- automated data analysis
- automatic document analysis
- best-fit analysis
- bottom-up analysis
- buckling analysis
- check analysis
- citation analysis
- cluster analysis
- comparative economic analysis
- compound flow analysis
- computer simulation analysis
- computer-aided design and analysis
- content analysis
- continuous analysis
- cost-effectiveness analysis
- deformation analysis
- design analysis
- diagnostic analysis
- dimensional analysis
- document analysis and synthesis
- dynamic analysis
- economic analysis
- end-point analysis
- error analysis
- facet analysis
- factory flow analysis
- failure cause analysis
- failure tree analysis
- fault analysis
- FEM analysis
- finite element analysis
- frequency analysis
- frequency domain analysis
- frequency-response analysis
- full grey scale analysis
- functional analysis
- grain size analysis
- grammatical analysis
- graphical analysis
- group analysis
- harmonic analysis
- incident sequence analysis
- information analysis
- integrated analysis
- kinematic analysis
- lexical analysis
- line analysis
- management analysis
- mean-value analysis
- metal surface alloy compositional analysis
- microprobe analysis
- model analysis
- morphological analysis
- multiple regression analysis
- network analysis
- numerical analysis
- on-board analysis
- operation analysis
- performance analysis
- predictive analysis
- production flow analysis
- quantitative analysis
- regression analysis
- reliability analysis
- sampling analysis
- semantic analysis
- semantic-syntactic analysis
- sensitivity analysis
- signature analysis
- simulation analysis
- spectral analysis
- speech analysis
- stability analysis
- statistical analysis
- strain analysis
- strength analysis
- stress analysis
- structural analysis
- structure analysis
- structured analysis
- subject analysis
- subpixel level full grey scale analysis
- syntactic analysis
- system analysis
- time domain analysis
- time-and-frequency analysis
- time-series analysis
- tooth contact analysis
- transient analysis
- trend analysis
- tridimensional analysis
- variation simulation analysis
- vectorial analysis
- vibration analysis
- what if analysis
- X-ray structural analysisEnglish-Russian dictionary of mechanical engineering and automation > analysis
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17 semantics
1) применительно к естественному языку - значение, смысл (смысловое содержание) языковой единицы (морфемы, слова, словосочетания и т. п.); семантика этих элементов должна учитываться, в частности, при распознавании речи и текста.Syn:2) в случае языка программирования - задаёт смысл (смысловое значение) его слов, символов и синтаксически правильных конструкций с точки зрения их поведения при исполнении программыAnt:см. тж. data semantics, denotational semantics, formal semantics, functional semantics, hermeneutics, operational semantics, semantic analysis, semantic error3) раздел семиотики, изучающий знаковые системы как средства выражения смыслаАнгло-русский толковый словарь терминов и сокращений по ВТ, Интернету и программированию. > semantics
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18 network
[ˈnetwə:k]activity network сетевой график algorithmic network вчт. алгоритмическая сеть argumented transition network вчт. расширенная сеть переходов associative network вчт. ассоциативная сеть augmented transition network вчт. расширенная сеть переходов backbone network вчт. базовая сеть branch network сеть филиалов broadband network широкополосная сеть broadcast network широковещательная сеть causal network причинно-следственная сеть communications network сеть передачи данных communications network сеть связи computer network вычислительная сеть computer network компьютерная сеть computer network сеть вычислительных машин connectional network соединительная сеть data network вчт. сеть передачи данных dealer network торговая сеть distribution network распределительная сеть distribution network сеть распространения extensional semantic network семантическая экстенсиональная сеть hybrid network гибридная сеть inference network вчт. сеть вывода ladder network вчт. многозвенная схема local network вчт. локальная сеть network вычислительная сеть network радиотрансляционная сеть network тех. решетчатая система network сетевой график network сеть (железных дорог, каналов и т. п.) network сеть, сетка; плетенка network вчт. сеть network сеть network сообщество network схема network эл. цепь, схема network data base вчт. сетевая база данных network file standard вчт. стандартный протокол сеевых файлов network file system вчт. сетевая файловая система network flow problem вчт. задача нахождения потока в сети network of queues вчт. многоканальная система обслуживания packet switching network вчт. сеть коммутации пакетов polled network вчт. сеть с опросом recursive transition network вчт. рекурсивная сеть переходов ring network вчт. кольцевая сеть road network дорожная сеть sales network сеть сбыта semantic network вчт. семантическая сеть slotted-ring network вчт. кольцевая сеть social network сеть социальных работников synchronous network вчт. синхронная сеть telecommunications network вчт. сеть связи telecommunications network система дальней связи telephone network телефонная сеть teleprinter network сеть телетайпной связи telex network система телексной связи terminal support network вчт. сеть поддержки терминалов traffic network транспортная сеть transition network вчт. сеть перехода состояний transport network вчт. транспортная сеть transport network транспортная сеть weighting network вчт. взвешивающая схема wideband network сеть связи с высокой пропускной способностью wideband network широкополосная сеть связи -
19 Cognition
As used here, the term "cognition" refers to all processes by which the sensory input is transformed, reduced, elaborated, stored, recovered, and used. It is concerned with these processes even when they operate in the absence of relevant stimulation, as in images and hallucinations....[G]iven such a sweeping definition, it is apparent that cognition is involved in everything a human being might possibly do; that every psychological phenomenon is a cognitive phenomenon. (Neisser, 1976, p. 4)Man is describable as a dual processor, dual memory system with extensive input-output buffering within each system. The input-output system appears to have substantial peripheral computing power itself. But man is not modeled by a dual processor computer. The two processors of the brain are asymmetric. The semantic memory processor is a serial processor with a list structure memory. The image memory processor may very well be a sophisticated analog processor attached to an associative memory. When we propose models of cognition it would perhaps be advisable if we specified the relation of the model to this system architecture and its associated addressing system and data structure. (Hunt, 1973, pp. 370-371)Historical dictionary of quotations in cognitive science > Cognition
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20 model
1) модель (1. упрощённое представление объекта, процесса или явления; структурная аналогия 2. макет 3. образец; эталон; шаблон 4. пример; тип 5. стиль; дизайн) || моделировать (1. создавать упрощённое представление объекта, процесса или явления; пользоваться структурной аналогией 2. макетировать 3. создавать образец, эталон или шаблон 4. пользоваться примером; относить к определённому типу) || модельный (1. относящийся к упрощённому представлению объекта, процесса или явления; использующий структурную аналогию 2. макетный 3. образцовый; эталонный; шаблонный 4. примерный; типовой)2) служить моделью; выполнять функции модели3) создавать по образцу, эталону или шаблону4) придерживаться определённого стиля; следовать выбранному дизайну•- 2-D model
- adaptive expectations model
- additive model of neural network
- analog model
- antenna scale model
- application domain model
- AR model
- ARCH model
- ARDL model
- ARIMA model
- ARMA model
- atmospheric density model
- autoregressive conditional heteroscedastic model
- autoregressive distributed lags model
- autoregressive integrated moving average model
- autoregressive moving average model
- band model
- behavioral model
- Benetton model
- Berkeley short-channel IGFET model
- binary model
- binary choice model
- Bohr-Sommerfeld model
- Bohr-Sommerfeld model of atom
- Box-Jenkins model
- Bradley-Terry-Luce model
- brain-state-in-a-box model
- breadboard model
- Brookings models
- BSB model
- business model
- CAD model
- capability maturity model
- carrier-storage model
- causal model
- censored model
- centralized model
- charge-control model
- Chen model
- classical normal linear regression model
- classical regression model
- client-server model
- CMY model
- CMYK model
- cobweb model
- collective-electron model
- color model
- compact model
- component object model
- computer model
- computer-aided-design model
- conceptual model of hypercompetition
- conceptual data model
- conductor impedance model
- congruent model
- connectionist model
- continuum model
- Cox proportional hazards regression model
- data model
- Davidson-Hendry-Srba-Yeo model
- descriptive model
- design model
- deterministic model
- DHSY model
- discrete choice model
- distributed component object model
- distributed computing model
- distributed lags model
- distributed system object model
- distribution-free model
- document object model
- domain model
- domain architecture model
- duration model
- dynamic model
- EER-model
- energy-gap model
- entity-relationship model
- ER-model
- error correction model
- errors-in-variables model
- experimental model
- extended entity-relationship model
- extended relational model
- extended relational data model
- extensional model
- ferromagnetic Fermi-liquid model
- file level model
- financial model
- finite-population model
- fixed-effects model
- flat Earth model
- flat free model of advertising
- formalized model
- fractal model
- frame model
- fuzzy model
- GARCH model
- generalized autoregressive conditional heteroscedastic model
- generalized linear model
- geometric model
- geometrical lags model
- gross-level model
- ground-environment model
- Haken-Kelso-Bunz model
- Heisenberg model
- heuristic model
- hierarchical data model
- HLS model
- holographic model
- HSB model
- HSV model
- Hubbard model
- huge model
- hybrid-pi model
- hypothesis model
- ideal model
- imaging model
- indexed colors model
- information model
- information-logical model
- intensional model
- intercept-only model
- ionospheric model
- irreversible growth model
- Ising model
- ISO/OSI reference model
- Klein model
- Kronig-Penney model
- L*a*b* model
- large model
- large-signal device model
- LCH model
- learning, induction and schema abstraction model
- life cycle model
- limited dependent variable model
- linear model
- linear probability model
- LISA model
- logical model
- logical-linguistic model
- logistic model
- logit model
- loglinear model
- Londons' model of superconductivity
- lookup-table model
- Lorentz model
- low-signal device model
- machine model
- macrolevel model
- magnetic hysteresis model
- magnetohydrodynamic plasma model
- mathematical model
- matrix-memory model
- medium model
- memory model
- MHD plasma model
- microlevel model
- Minsky model
- Minsky frame model
- mixed model
- molecular-field model
- moving average model
- multiple regression model
- multiplicative model
- nested model
- network model
- network data model
- non-nested model
- non-parametric model
- N-state Potts model
- N-tier model
- null model
- object model
- object data model
- one-dimensional model
- one-fluid plasma model
- operations model
- optimizing model
- parabolic-ionosphere model
- parametric model
- parsimonious model
- partial adjustment model
- phenomenological model
- physical model
- pilot model
- Pippard nonlocal model
- plant model
- Poisson model
- polar model
- polynomial lags model
- postrelational model
- postrelational data model
- Potts model
- predictive model
- Preisach model
- preproduction model
- price model of advertising
- probabilistic model
- probit model
- proportional hazard model
- proportional-odds model
- prototype model
- quadratic model
- qualitative dependent variable model
- quantum mechanical model of superconductivity
- quasi-equilibrium model
- quasi-linear model
- random coefficients model
- random-effects model
- register model
- relational model
- relational data model
- relative model
- representative model
- response-surface model
- RGB model
- Ridley-Watkins-Hilsum model
- rival models
- Rössler model
- RWH model
- saturated model
- scalar model
- SCSI architecture model
- semantic model
- semiotic model
- sharply bounded ionosphere model
- simulation model
- single-ion model
- Skyrme model
- small model
- small-signal device model
- solid model
- spherical Earth model
- state-space model
- statistical model
- stochastic model
- Stoner-Wohlfart model
- structural model
- stuck-at-fault model
- surface model
- symbolic model
- symbolic-form model
- synergetic model
- system model
- system object model
- test model
- thermodynamical model
- three-tier model
- tobit model
- transistor model
- translog model
- tropospheric model
- true model
- truncated model
- two-dimensional model
- two-dimensional regression model
- two-fluid model of superconductivity
- two-fluid plasma model
- two-tier model
- Van der Ziel's noise model
- variable parameter model
- vector model
- wire-frame model
- working model
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