semantic processing

- administrative processing

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   1) Programs and the Complexity of Human Mental Processes

   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)

   2) Artificial Intelligence Is an Engineering Discipline

   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)

   3) A Sceptical View of Artificial Intelligence

   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, Eventually

   Just 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 Program

   Many 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)

   6) The Meaning of a Symbolic Description

   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)

   7) The Principle of Artificial Intelligence

   [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)

   8) Artificial Intelligence Recognizes the Need for Knowledge in Its Systems

   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 Form

   The 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)

    10) There Are Many Types of Reasoning

   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)

    11) Programs Are Beginning to Do Things That Critics Have Asserted to Be Impossible

   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)

    12) The Synthesis of Man and Machine

   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)

    13) The Thesis of Good Old-Fashioned Artificial Intelligence (GOFAI)

   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 Formation

   It 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)

    15) Four Kinds of Artificial Intelligence

   We might distinguish among four kinds of AI.

   ♦ Nonpsychological 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.

   ♦ Weak Psychological AI

   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.

   ♦ Strong Psychological AI

... 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)

   ♦ Suprapsychological AI

   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 Contexts

   Even 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)

    17) Form and Content Are Not Fundamentally Different

   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)

    18) The Assumption That the Mind Is a Formal System

   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 Intelligence

   The 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)

    20) Mathematical Logic Provides the Basis for Theory in AI

   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 Propositions

   In 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)

    22) Definitions of Artificial Intelligence

   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)

    23) The Computer Can Not Be a Model of the Mind

   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)

    24) Computers Will Not Always Be Inferior to Human Brains

   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)

1) единица, число 1 ( наименьшее положительное целое число)

2) единица (физической) величины; единица измерения ( величины)

3) элемент; компонент

4) нейрон

5) блок; узел; модуль; секция; звено

6) прибор; устройство

7) аппарат; установка

•

- unit of measure

1) Компьютерная техника: Neuro Semantic Programming, Numeric Space

2) Авиация: национальная программа безопасности (National Security Programme)

3) Американизм: Not Separately Priced

4) Военный термин: NORAD/USSPACECOM, Navy Support Plan, Navy standard part, Nitro Spring Pack, non self-propelled, nuclear strike plan, nuclear support platoon

5) Техника: navigational satellite program, network signal processor, nuclear space power system

6) Сельское хозяйство: НПС, некрахмалистые полисахариды (non-starch polysaccharides)

7) Коммерция: Nearest Shipping Port

8) Бухгалтерия: Net Selling Price

9) Грубое выражение: Nationally Stupid Party

10) Телекоммуникации: Network Service Protocol

11) Сокращение: National Space Program, Navy Special Projects office (USA), Non-volatile, Serially Programmable

12) Вычислительная техника: Nationally Significant Number, Network Service Point, native signal processing, протокол сетевого обслуживания, Network Services Protocol (DNA), Name Service Protocol (DNS), Native Signal Processing (Intel, CPU), network service provider, обработка сигналов с использованием ресурсов ЦП, протокол обслуживания сети вычислительных систем

13) Фирменный знак: Natures Sunshine Products

14) Сетевые технологии: National Service Provider, Network Services Protocol

15) Полимеры: nonswelling and paintable

16) Автоматика: non-service part

17) Океанография: Neurological Shellfish Poisoning

18) Химическое оружие: Non-Stockpile Program

19) Авиационная медицина: nystagmus slow phase

20) Макаров: nuclear steam plant

21) Расширение файла: Native Signal Processing (Intel)

22) Светотехника: линза для получения узкого луча

23) Электротехника: network status processor

24) NYSE. Northern States Power Company of Minnesota

1) Компьютерная техника: Neuro Semantic Programming, Numeric Space

2) Авиация: национальная программа безопасности (National Security Programme)

3) Американизм: Not Separately Priced

4) Военный термин: NORAD/USSPACECOM, Navy Support Plan, Navy standard part, Nitro Spring Pack, non self-propelled, nuclear strike plan, nuclear support platoon

5) Техника: navigational satellite program, network signal processor, nuclear space power system

6) Сельское хозяйство: НПС, некрахмалистые полисахариды (non-starch polysaccharides)

7) Коммерция: Nearest Shipping Port

8) Бухгалтерия: Net Selling Price

9) Грубое выражение: Nationally Stupid Party

10) Телекоммуникации: Network Service Protocol

11) Сокращение: National Space Program, Navy Special Projects office (USA), Non-volatile, Serially Programmable

12) Вычислительная техника: Nationally Significant Number, Network Service Point, native signal processing, протокол сетевого обслуживания, Network Services Protocol (DNA), Name Service Protocol (DNS), Native Signal Processing (Intel, CPU), network service provider, обработка сигналов с использованием ресурсов ЦП, протокол обслуживания сети вычислительных систем

13) Фирменный знак: Natures Sunshine Products

14) Сетевые технологии: National Service Provider, Network Services Protocol

15) Полимеры: nonswelling and paintable

16) Автоматика: non-service part

17) Океанография: Neurological Shellfish Poisoning

18) Химическое оружие: Non-Stockpile Program

19) Авиационная медицина: nystagmus slow phase

20) Макаров: nuclear steam plant

21) Расширение файла: Native Signal Processing (Intel)

22) Светотехника: линза для получения узкого луча

23) Электротехника: network status processor

24) NYSE. Northern States Power Company of Minnesota

1 registry (office) (oficina).

registro civil registry (office)

registro de comercio o mercantil business registry office

registro de la propiedad land registry office

registro de la propiedad industrial/intelectual trademark/copyright registry office

2 registration.

llevar el registro de algo to keep a record of something

3 register (libro).

registro parroquial parish register

4 search, searching.

efectuaron un registro domiciliario they searched his/her/etc home

5 bookmark.

6 record (computing).

7 register (linguistics & music).

8 inspection, inquisition, search.

9 entry.

10 damper.

11 voice range, range.

1^st person singular (yo) present indicative of spanish verb: registrar.

* * *

► nombre masculino

1 (inspección) search, inspection

2 (inscripción) registration, recording; (matriculación) enrolment (US enrollment), registration

3 DERECHO (oficina) registry; (libro) register

4 MÚSICA register (de órgano) stop

5 INFORMÁTICA register

6 TÉCNICA inspection hole

\

tocar todos los registros figurado to pull out all the stops

registro civil births, marriages and deaths register 2 (oficina) registry office

registro de la propiedad land registry

registro electoral electoral roll

registro mercantil business register

* * *

1) register

2) registry

3) record

4) search

* * *

1) (=acción) registration, recording

2) (=libro) register; (Inform) record

3) (=lista) list, record; (=apunte) note

4) (=entrada) entry

5) (=oficina) registry, record office

6) (=búsqueda) search; (=inspección) inspection

7) (Mús) (=grabación) recording

8) (Mús) (=timbre) [de la voz] register; [del órgano] stop; [del piano] pedal

- adoptar un registro muy raro

- tocar todos los registros

9) (Téc) manhole

10) (Ling) register

11) (Dep) (=marca) personal best; (=récord) record

12) [de reloj] regulator

13) (Tip) register

14) And, Cono Sur (=tienda) wholesale textiles store

* * *

1) ( libro) register; ( acción de anotar) registration; ( cosa anotada) record, entry

- registro civil

- registro de la propiedad

- registro de patentes y marcas

2) ( por la policía) search

- registro domiciliario

3) ( de reloj) regulator

4) (Mús)

a) (de voz, instrumento) range

b) ( pieza - de órgano) register, stop; (- de piano, clavicordio) pedal

c) ( tono) register

5) (Ling) register

6) (Tec) ( abertura) inspection hatch; ( de agua) (Col) stopcock, shutoff valve (AmE)

* * *

1) ( libro) register; ( acción de anotar) registration; ( cosa anotada) record, entry

- registro civil

- registro de la propiedad

- registro de patentes y marcas

2) ( por la policía) search

- registro domiciliario

3) ( de reloj) regulator

4) (Mús)

a) (de voz, instrumento) range

b) ( pieza - de órgano) register, stop; (- de piano, clavicordio) pedal

c) ( tono) register

5) (Ling) register

6) (Tec) ( abertura) inspection hatch; ( de agua) (Col) stopcock, shutoff valve (AmE)

* * *

registro¹

¹ = rec (record), record, tuple, booking record.

registro²

² = register, register, registry, records centre.

registro³

³ = accessioning, stock recording, registration.

registro⁴

⁴ = manhole.

* * *

A (libro) register; (acción de anotar) registration; (cosa anotada) record, entry

(libro) register of births, marriages and deaths; (oficina) registry, registry office ( BrE)

(libro) land register; (oficina) land registration office, land registry ( BrE)

patent office

patent office

( Chi) electoral roll o register

parish register

B (por la policía) search

orden de registro search warrant

la policía ha efectuado 300 registros domiciliarios the police have carried out searches on 300 houses

C (de un reloj) regulator

D ( Mús)

1 (de una voz, un instrumento) range

2 (pieza — de un órgano) register, stop; (— de un piano, clavicordio) pedal

3 (tono) register

E ( Ling) register

F ( Tec)

1 (abertura) inspection hatch

2 ( Col) (de agua) stopcock, shutoff valve ( AmE)

G ( Arg) driver's license ( AmE), driving licence ( BrE)

* * *

1ª persona singular (yo) presente indicativo

3ª persona singular (él/ella/usted) pretérito indicativo

a) ‹nacimiento/defunción/patente› to register

b) ‹sonido/temperatura› to record;

◊ estaba registrando mis cajones (fam) he was going through my drawers

◊ registro civil ( oficina) registry, registry office (BrE)

◊ orden de registro search warrant

* * *

registro nm

1. [oficina] registry (office)

registro civil registry (office);

registro de comercio trade register office;

registro mercantil trade register office;

registro de la propiedad land records office, Br land registry office;

registro de la propiedad industrial trademark registry office;

registro de la propiedad intelectual copyright registry office

2. [libro] register;

inscribir a alguien en el registro civil to register sb in the register of births, marriages and deaths

registro parroquial parish register

3. [inscripción] registration;

llevar el registro de algo to keep a record of sth

4. [inspección] search;

una orden de registro a search warrant;

procedieron al registro de la fábrica they carried out a search of the factory;

efectuaron un registro domiciliario they searched his/her/ etc home

5. [de libro] bookmark

6. Informát [en base de datos] record

7. Ling register

8. Mús [notas] register

9. Mús [en órgano] stop, register;

tocar todos los registros to pull out all the stops

10. Tec [abertura] inspection hatch

11. Tec [llave] stopcock

* * *

1 ( archivo) register

2 de casa search

tocar todos los registros fig fam pull out all the stops fam

* * *

registro nm

1) : register

2) : registration

3) : registry, record office

4) : range (of a voice or musical instrument)

5) : search

* * *

registro n

1. (examen) search

2. (inscripción) registration

3. (libro) register

- a priori method

- abbreviated method

- abridged method

- absolute method

- adaptive method

- adaptive smoothing method

- ADI method

- adjoint method

- adobe blasting method

- aero-projection method

- algebraic method

- algorithmic method

- alternating method

- alternating-variable descent method

- analog method

- analytical method

- angular modulation method

- approximate estimation method

- approximation-function method

- approximative method

- area method

- area moment method

- ascent method

- asymptotically efficient method

- averoid method

- axiomatic method

- axonometric method

- backward-sweep method

- balayage method

- Barrelet method of zeroes

- barrier method

- baseband recording method

- block-diagram method

- bordering method

- Borrmann method

- bottle method

- boundary integral method

- building-block method

- caisson method

- cascade method

- case method

- catastrophe method

- category method

- centroid method

- chain method

- chaining method

- chord method

- climb milling method

- clustering method

- cofactor method

- coincidence method

- collocation method

- colorimetric method

- combinatorial method

- comparative method

- comparison method

- complete elimination method

- complexometric method

- composite value method

- computing method

- conditionally stable method

- conductance-measuring method

- cone method

- conformal mapping method

- conjugate-gradient method

- constant-fraction method

- constructive method

- continuation method

- contour integral method

- convergence method

- convergent method

- convex simplex method

- covariant method

- crossed beam method

- crude method

- cut-and-try method

- cutting-plane method

- cyclic method

- data enrichment method

- decomposition method

- deductive method

- delta method

- depth-first method

- describing function method

- diagonalization method

- dichotomy method

- difference factorization method

- differential-equations method

- diffusion method

- digital method

- dilution method

- dimensional method

- direct method of measurement

- direct method

- direct search method

- dispersion method

- distribution-free method

- dot alloying method

- double-description method

- dry method

- dual simplex method

- duality method

- Dumas method

- dye-penetrant method

- efficient method

- efflux method

- electrophoretic method

- elementary method

- elimination method

- embedded method

- empirical method

- energy method

- equal-probability selection method

- equal-strain method

- equation method

- ergodic method

- error method

- error of method

- escalator method

- estimation method

- exact method

- exclusion method

- exhaustion method

- expansion method

- experimental method

- exterior point method

- extrapolation method

- factor comparison method

- factorial method

- factorization method

- falling body method

- fall-of-charge method

- false position method

- finitary method

- finite difference method

- fitting method

- fixed-point method

- floatation method

- floating-zone method

- forecasting method

- formal method

- fractional step method

- freehand method

- functional method

- fundamental method

- game-theoretic method

- general method

- genetic method

- geometrical method

- global balance method

- gradient-search method

- graph method

- graphic method

- grapho-numerical method

- gravimetric method

- gray-wedge method

- grid method

- Griess-Ilovai method

- grouping method

- half-and-half method

- half-deflection method

- half-split method

- heuristic method

- hill-climbing method

- Hopkinson split-bar method

- horn-and-lens method

- hydraulic fill method

- hypothetico-deductive method

- idealized method

- identification method

- immersion method

- inaccurate method

- incremental method

- index method

- indirect method of measurement

- indirect method

- inductive method

- inexact method

- infinitesimal method

- informal method

- integral estimation method

- integral method

- integro-interpolation method

- interior point method

- interpretation method

- inverse matrix method

- inverse-scattering method

- inversion method

- isocline method

- isolation method

- iterated-interpolation method

- iteration method

- jacknife method

- kernel of summation method

- kick-sorting method

- knapsack method

- laboratory method

- laborious method

- Lagrange's method of multipliers

- learning method

- least sample value method

- leveling method

- limited information method

- linking method

- load factor method

- load-factor method

- local method

- logical method

- logical-and-probabilistic method

- logistic method

- lost wax method

- machining method

- magnetic particle method

- manufacturing method

- mapping method

- marginal method

- Markowitz method

- matching method

- mathematically deductive method

- matrix method

- maximum likelihood method

- mean-value method

- measurement method

- method of abstraction

- method of acyclic models

- method of approximation

- method of arithmetic means

- method of arithmetization

- method of averages and ranges

- method of averages

- method of backward induction

- method of balayage

- method of barriers

- method of basic series

- method of bearings

- method of catastrophes

- method of characteristic functions

- method of characteristics

- method of choice

- method of collapsed strata

- method of comparison

- method of complex gradients

- method of continuation

- method of continuity

- method of contrasts

- method of convex ascent

- method of correction

- method of countable coverings

- method of counterexamples

- method of cutting and gluing

- method of determinants

- method of diagrams

- method of direct elimination

- method of estimation

- method of exhaustion

- method of extrapolation

- method of extremal metric

- method of forcing

- method of forecasting

- method of fraction levelling

- method of gisements

- method of golden section

- method of graphs

- method of harmonic balance

- method of homology

- method of incremental rates

- method of indicators

- method of induction

- method of inductive inference

- method of inversion

- method of isoclines

- method of iterations

- method of krakowians

- method of leading variables

- method of lines

- method of loci

- method of martingale

- method of mathematical induction

- method of multipliers

- method of normals

- method of numbering

- method of observation

- method of operators

- method of optimum allocation

- method of ordering

- method of orthogonal contrasts

- method of parabolas

- method of parametrix

- method of partial integration

- method of partition

- method of penultimate remainder

- method of potentials

- method of prediction

- method of principal components

- method of proof

- method of proportional control

- method of quantiles

- method of quickest descent

- method of randomization

- method of raviness

- method of rectangles

- method of reiteration

- method of representative sample

- method of residues

- method of revolution

- method of rotating coordinates

- method of scoring

- method of search

- method of selected points

- method of semiaverages

- method of separation of variable

- method of smoothing

- method of solution

- method of spherical means

- method of substitution

- method of successive corrections

- method of summability

- method of sweeping

- method of tangent parabolas

- method of transformation

- method of trapezoids

- method of unconditional optimization

- method of verification

- method of weight functions

- midrank method

- minimal residual method

- minimax method

- mirror-image method

- modified method

- momentum-transfer method

- moving average method

- moving-average method

- multiextremal method

- multiple correlation method

- multiplex method

- multistep method

- nephelometric method

- net-point method

- neutral-points method

- nonconstructive method

- noncovariant method

- nonlinear method

- nonparametric method

- nonprobabilistic method

- nonrank method

- nonrecursive method

- nonsequential method

- normal approximation method

- number-theoretical method

- numerical graphical method

- objective method

- obstruction method

- offset method

- offset-signal method

- one-sweep method

- operational method

- operations research method

- operator method

- optimal method

- optimum-seeking method

- ordinary least squares method

- orthogonalization method

- orthogonalized-plane-wave method

- parabolic extrapolation method

- paramagnetic-resonance method

- particle method

- particle-in-cell method

- path-of-steepest-ascent method

- penalty function method

- plunge-cut method

- point-estimation method

- polar method

- pole-and-ray method

- postulational method

- powder method

- precise method

- primal-dual method

- principal-factor method

- problem-solving method

- processing method

- prognosis method

- proof method

- proportional parts method

- pseudoviscosity method

- pulse method

- pulse-echo method

- quadrature method

- quality control method

- quantal-response method

- quasianalytic method

- quasilinearization method

- quota method

- radiation method

- radiometric method

- random search method

- ranking method

- raster-scan method

- ray-trace method

- real method

- recursion method

- refletion method

- regression method

- regula falsi method

- regular method

- rejection mask method

- reliability method

- representative method

- residue method

- response surface method

- retardation method

- retract method

- rigorous method

- robust method

- root-locus method

- root-sum-of-squares method

- roster method

- rotating-crystal method

- rotation method

- rough-and-ready method

- routine method

- row-by-row method

- Runge-Kutta method

- saddle point method

- saddle-point method

- sampling method by attributes

- saturation method

- scale factor method

- sedimentaion method

- self-checking method

- self-consistency method

- semantic method

- semigraphical method

- semigroup method

- separation method

- set-theoretical method

- shadow method

- shaft sinking method

- shake method

- shock-capturing method

- sieve method

- similitude method

- similitude method

- simple summation method

- simplex method

- simplified method

- simulation method

- singular perturbation method

- singularity method

- slope-deflection method

- smoothing method

- solid straircase method

- solution method

- spectroscopic method

- speedup method

- spiral-scan method

- split-half method

- split-plot method

- spot-scan photomultiplier method

- stable method

- standard inspection method

- standard method

- stationary phase method

- step-back method

- stepping-stone method

- stepwise method

- stochastic approximation method

- stochastic method

- stylus method

- subjective method

- substitution method

- succession-sweep method

- successive exclusion method

- summability method

- sweeping-out method

- symmetrization method

- synchronous storage method

- synthetic method

- systematic method

- target method

- test-line method

- thermal reduction methods

- time-of-flight method

- total value method

- total-strain method

- transcendental method

- transformation method

- transition state method

- tree method

- tree-search method

- trial method

- trial-and-error method

- triangulation method

- trilateration method

- truncation method

- truth-table method

- try-and-error method

- Tukey-Cooley method

- Tukey-Sand method

- two-dimensional method

- two-stage least-square method

- two-sweep method

- univariate method

- up-and-down method

- value-iteration method

- variable metric method

- variable-phase method

- visual method

- volumetric method

- weighted least-squares method

- Whitham's method

- wobbulator method

- worst-case method

- X-ray diffraction method

- zero-beat method

   1) Grammar as Analogous to a Scientific Theory

   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)

   2) Native Speakers and Their Grammar

   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)

   3) The Reduction of Transformation Rules In a Science of Grammar

   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 Performance

   he 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)

   5) The Terminologies of Formal Grammar

   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)

   6) The Theory of Grammar

   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)

1) сеть (железных дорог, каналов, трубопроводов и т. п.)

2) электрическая сеть, сеть электроснабжения, сеть электропитания

3) энергосистема

4) сеть ( связи)

5) схема; цепь; контур

6) эл. многополюсник; четырехполюсник

7) сетевой график

8) вычислительная сеть; сеть ЭВМ

9) геофиз. сеть наблюдений; сеть опорных пунктов

10) бтх внутриклеточный каркас

1) единица; единое целое

2) единица измерения

3) устройство; узел; блок; прибор; звено; элемент (см. тж component, device, element)

4) компонента программы, модуль

•

- actuating unit