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Как известно, для письменной научной прозы характерны сложные синтаксические конструкции с причастными, герундиальными и инфинитивными оборотами. Задача заключается в том, чтобы преобразовать сложные предложения в более простые, а последние, в свою очередь, сократить до "ядра предложения", то есть до слов, несущих основную смысловую нагрузку (kernel sentences). Для того, чтобы сохранить логическую структуру оритинала,необходимо соединить предложения (kernel sentences) в логически построенный ряд с помощью связующих слов (connective words). К связующим словам можно отнести такие как: then, therefore, thus, because, yet, moreover, as well as, also, in the case of, consequently, hence, in order to, since, that, that's why. nevertheless, however, besides, in addition to, etc.

Кроме связующих слов (connective words) в аннотации и реферате употребляются слова и словосочетания, которые выражают мнение автора (авторов) с одной стороны, и мнение референта с другой. К ним можно отнести такие слова и словосочетания, как: it is well known that, as it is known, according to, in his (her, their) opinion, to my mind, on the contrary, in turn, in the sense that, it is (was)... that, likewise, in short, at the angle of, from his (her, their) point of view on...etc.

В качестве примера для иллюстрации вышесказанного приведем анализ метода работы над той же научной статьей "Machine translation: a hybrid view" by Yorick Wilks.

I. The items of the plan:* (* "the items of the plan" or. in other words, '-the titles of the paragraphs").

1. MT in wide use nowadays

2. High-quality, fully automatic translation an illusive goal for the present.

3. Statistical methods as the way out of the deadlock.

4. Machine-assisted MT.

5. AI (Artificial Intelligence) about knowledge-based systems as the key to MT.

6. Linguistics and MT.

7. A striking achievement of IBM researchers.

8. Etc.............................................................................................

II. Kernel sentences illustrating the items of the plan:

1. Everyday MT systems produce fully automatic translations at the Federal Translation Division in Dayton, in Ohio, and at the European Commission in Luxembourg.

2. High quality, fully automatic translation has not been produced up to now.

3. The way out of the deadlock was empirical, i.e. statistical methods. They took as data very large text corpora.

4. The escape from the very same deadlock was to move to machine-assisted MT.

5. AI argued that knowledge-based systems were the key to MT. They had failed to deliver knowledge bases of sufficient size.

6. Linguistics was in a far worse position than AI. It could not weather the statistical onslaught at all.

7. IBM researchers achieved the striking results. They could produce 50-plus percent of correctly translated sentences of unseen texts in a trained corpus.

Etc.........................................

III. Joining kernel sentences together with connective words:

It is well known that every day MT systems produce fully automatic translations at the Federal Division in Dayton, in Ohio, and at the European Commission in Luxembourg. Yet high-quality, fully automatic translation has not been produced up to now. According to IBM laboratory researchers the way out of the deadlock was empirical, i.e. statistical methods that took as data very large corpora. Besides, the escape from the very same deadlock was, in their opinion, to move to machine-assisted MT. On the contrary, AI argued that it was knowledge-based systems that were the key to MT. But they had failed to deliver knowledge bases of sufficient size. Linguistics was in a far worse position than AJ in the sense that it could not weather the statistical onslaught at all. IBM researchers, in turn, achieved striking results. Thus they could produce 50-plus percent of correctly translated sentences from unseen texts in a trained corpus. Etc.........................................................................................................

Кроме письменного, существует так же и устное аннотирование научных документов, т.е. умение передать в устной форме его основные положения, характерные особенности и выводы. Для формирования умений и навыков устного аннотирования здесь приведены также ряд текстов и упражнений. Одним из видов таких упражнений является rendering (т.е. передача другими словами в устной форме содержания научного текста).

To our students' attention.

This reader of translating, annotating, and rendering of computer terminology is an outgrowth of our department experience in teaching courses for post-graduates who desperately need a workbook. The work before you is a product of constant revision and change dictated by the needs and critical evaluations of our post-graduate students over the past two years.

There are two basic assumptions underlying the design of the work. One is that computer, communication and math terminology can be taught as a language emphasizing logical and rational understanding of the terms rather than just memorization of them.

Another assumption is that the main aim of this work is preparation for taking master degree in Professional English.

That is why this work tries to cover the four main aspects of the exam. The first two chapters are fully devoted to written (prepared) and oral (at sight) translating of professional texts.

The third chapter deals with summarizing and annotating of the original professional texts.

The fourth part trains rendering. We consider this part of the work to be equally important as it encourages speaking skills

The book is designed both for classroom use and for independent study.

We hope that it will be helpful to you not only in preparation for your MD but also in your every day professional English improving.

With best wishes Authors

CHAPTER 1

In this chapter you will gain practice in reading, understanding and written translating the computer language in context. Before starting to work on this chapter read the recommendations and advice on p.4 very attentively.

Unit 1

1. Read and translate in written form text №1 using dictionary if necessary.

ТЕХТ №1

Some words about Internet

The Internet is revolutionizing commerce. It provides the first affordable and secure way to link people and computers spontaneously across organizational boundaries. This is spawning numerous innovative enterprises—virtual companies, markets, and trading communities,

But the Internet's potential is imperiled by the rising specter of digital anarchy:

closed markets that cannot use each other's services; incompatible applications and frameworks that cannot interoperate or build upon each other; and an array of security and payment options that confuses consumers.

One solution to these problems is an object oriented architectural framework for Internet commerce. Several major vendors of electronic-commerce solutions have announced proprietary versions of such a framework. The major platforms are:

• IBM Commerce Point

• Microsoft Internet Commerce Framework

• Netscape ONE (Open Network Environment)

• Oracle NCA (Network Computing Architecture)

• Sun/Java soft JECF (Java Electronic Commerce Framework).

Recently, four of these companies have agreed to support a common distributed object model based on CORBA HOP (Common Object Request Broker Architecture Internet InterORB Protocol). Yet for commerce on the Internet to thrive, such systems must also interoperate at a business application level. (For more information see the "Major E-Commerce Platforms" sidebar.) A consumer or business using one framework should be able to shop for; purchase, and pay for goods and services offered on a different framework. This is currently not possible.

In response. Commerce Net is organizing Eco System, a cross-industry effort to build a framework of frameworks, involving both e-commerce vendors and end users. This project is challenging from a technical perspective because information technology is moving so fast that there's seldom time for even de facto standards to emerge. Instead, we must deal with de facto interoperation—making incompatible products already in the marketplace communicate. Our philosophy is simple: Protocols, formats, and the like should not hinder business.

The success of this process clearly depends on market leaders in each area participating actively on their respective task forces. Admittedly, in past battles for market dominance (such as in operating systems and desktop PCs), it was difficult to bring leading players to the table. For robust Internet commerce, however, interoperability is so fundamental that we have to turn the concept of openness on its head—it's not just publishing an API. Everyone's software has to work together because no single company can control what platform its customers will use.

2. Read and translate text №2 without a dictionary.

TEXT №2

OVERVIEW

As proposed, Eco System will consist of an extensible object-oriented framework (class libraries, application programming interfaces, and shared services) from which developers can assemble applications quickly from existing components. These applications could subsequently be reused in other applications.

We are also developing a Common Business Language (CBL) that lets application agents communicate using messages and objects that model communications in the real business world. A network services architecture (protocols, APIs, and data formats) will insulate application agents from each other and from platform dependencies, while facilitating their interoperation

Unit 2

I. Read and translate in written form the text using a dictionary if necessary. Pay attention to the underlined words.

TEXT №1

CSLAB

With its network-centric computing model, support for graphical user interfaces, and availability of integrated development environments, Java makes it easy to produce simple applets that animate or help visualize details of mathematical entities, such as algorithms. But despite the dynamic and interactive nature of their underlying language, such applets, are static and restricted in scope—in fact, they offer little improvement over much older algorithm animation efforts.

Applets typically perform a fixed set of operations, and users cannot easily reconfigure such a system to perform "what if experiments or gather statistical or timing information for an experiment. Thus, while Java and the Web support dynamic interactions and enable exciting capabilities in educational software, few applications built on them exploit these capabilities. Existing educational applets are interesting exercises in Java programming, but they lack several essential capabilities that would allow them to be used effectively as components within a computer-assisted education infrastructure.

Compare applet capabilities to those of systems such as Matlab and Math used extensively in teaching and research. These powerful systems meet the needs of certain user communities, and their many specialized toolkits allow for rapid system prototyping and experimentation in areas such as signal processing and control systems. Nevertheless, they have two drawbacks: Building new applications with them requires significant programming efforts, and embedding them as executable content in Web pages is difficult because they lack support for Web interactions.

To overcome these limitations, we created CSLab, a proof-of-concept prototype of a simulation environment that is easy to use, dynamic, and Web-accessible. It is designed from the ground up to exploit the inherent dynamic behavior of Java and the Web and to let end users capitalize on this behavior. We originally envisioned it as a laboratory course in computer science, similar to courses in the physical sciences. Our goal was to build a framework within which to create, configure, and execute experiments, but instead of the physical sciences we focused on experimental algorithmic.

It was clear from the start that to support the desired level of dynamic behavior and interaction, CSLab would have to provide

• an intuitive visual interface to let users configure experiments with point-and-click actions;

• the functionality of a script file—that is, the ability to systematically configure and run a family of related experiments;

• an open architecture so that new building blocks can be designed and added to the system with minimal effort and service disruption; and

• persistent objects so that a workbook of experiments and their results can be maintained.

This last facility would need the capability to save and restore both passive objects such as experimental data and active objects such as the experimental setup.

NOTES:

1. Math entities (n.) — математический модуль, объект, примитив.

2. Applet (n.) — встраиваемое приложение, активный объект, апплет.

3. Toolkit (n.) — пакет разработчика.

II. Read and translate text №2 without a dictionary.

TEXT №2

Various stand-alone or networked "virtual laboratories" already exist. The environments found in the instructional products of the Shodor Education Foundation (http://www.shodor.org) provide preconfigured experiments. All you do is supply input parameters using a form-based interface. The experiment itself is a black box that you cannot manipulate or modify to answer "what if questions. Such rigidity is characteristic of most existing Web-based instructional systems. The Biology Workbench (http://biology.ncsa.uiuc.edu/BW/BW.cgi) provides a point-and-click interface for rapid access to biological databases and analysis tools. Also rigid in structure, it illustrates the need for customizable front ends. The key difference between these systems and CSLab is that the CSLab executable is a framework for creating various simulation experiments using available modules.

Answer the following questions:

1. What do they mean by " preconfigured experiments "?

2. What is the meaning of the term " rigidity "?

3. What is the key difference between these systems and CSLab?

Unit 3

I. Read and translate in written form text №1 using a dictionary if necessary. Pay attention to the notes.

TEXT №1

Consumer Privacy

Consumer privacy is another important issue. There are good and bad uses for the extensive personal data now online. For instance, companies can use this data to reduce search costs by notifying customers about merchandise they are likely to want based on their customer profiles. The information can also be sold to third parties. Neither of these situations is intrinsically undesirable, but given the amount of data now available, customers want — and should have — a say in how information will be used.

This issue's resolution is likely to be market-driven: If individual privacy remains a major consumer concern, e-commerce will not flourish as predicted. Organizations like a Trust, formed by the Electronic Freedom Foundation and CormmerceNet, are emerging to provide trust mechanisms that guarantee individual privacy. Companies or organizations participating in this program must display "trustmarks" describing how they handle the data of users visiting their sites. Categories of information exchange include anonymous or none, one-to-one (between the customer and site owner), and third party.

Характеристики

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