[ اللغة الانجليزية ] : Third year - 6th level - 0ld plan @.@

[aio0osh]

طيب بنات ممكن احد ينسخ القطعه ويلصقها هنا لان مافتحت معي

[maziona]

ترجمه والا كومبرهنشين ..

طيب باقي الاسئله وش بتكون ؟؟

مشكوره ع الرد .. وبالتوفيق


ايوه صح بنات انا بعد مافتحت عندي ياليت احد ينسخها ويحطها هنا

[Diamond brooch]

Machine Translation
Exhilarating goals, serious disappointments, significant successes: all have accompanied machine translation in its twentieth- and twenty-first century existence. The early goal––fully automated translation of a quality equal to that achieved by human translators––has proven elusive, and some researchers doubt that it is feasible. Nonetheless, machine translation and its sister, machine-aided human translation, must be counted a success if measured by their effectiveness in extensive use by multinational companies, governments, and the European Commission. Computer tools that aid human translators by automating some parts of the translation process, coupled with machine translation programs whose output requires final human editing, have together met a worldwide demand that is increasing faster than the supply of human translators.
The history of machine translation in modern times begins in 1947 with a memo from Warren Weaver, then director of the Natural Sciences Division of the Rockefeller Foundation, to Norbert Wiener, a well-known computer scientist (then called cyberneticist): “I have wondered if it were unthinkable to design a computer which would translate. Even if it would translate only scientific material (where the semantic difficulties are very notably less), and even if it did produce an inelegant (but intelligible) result, it would seem to be worth while” (Hutchins 1997:197).
Only two years later, in 1949, the New York Times reported that the computer of the United States Bureau of Standards Laboratory, which it called the ‘electric brain’, would be able to translate foreign languages (Hutchins 1997:197–203).
Early machine translations proceeded word for word from the original language into the target language (in those days of the Cold War often Russian to English), a process that ignores the complexity of language structure and results in an inaccurate and barely readable translation. With the development of more sophisticated tools for computerized syntactic analysis, many researchers assumed that success was near at hand. Enthusiasm for the goal of automatic, high-quality machine translation fueled generous government and private funding of research and development of translation systems through the early 1960s. Dissenting voices such as that of Yehoshua Bar-Hillel (1960) that this perfectionist goal was unrealistic were at first not heeded.
In 1966, the National Academy of Sciences Automatic Language Processing Advisory Committee (ALPAC) reported that the prospects for fully automated high-quality, general-purpose machine translation were dim, causing immediate and massive cutbacks in funding for research in the field.
By the early 1970s, interest in the field revived, but accurate and publishable translation untouched by human hands was no longer the only goal; human–machine cooperation was envisioned and the creation of automated tools for translators became a special field within machine translation research.
The appearance of interactive desktop computer workstations greatly facilitated the development of translator’s aids, and sophisticated ‘translator’s workbenches’ were engineered. Current versions of these translator’s workbenches include large databases of specialized vocabulary and high-quality desktop publishing tools, as well as ‘translation memory’, which enables the translator to store and reuse existing translations.
Currently used machine translation systems such as Logos, Metal, Systran, and PARS can translate even complex texts in a limited and defined domain, or subject area. Nonetheless, they require the services of humans to pre-or postedit the texts. In many important ways, the quality of machine translation has not risen appreciably since the 1970s, and the need for human editing is projected to continue.
At the other end of the spectrum, uses have been discovered for the rough, often inaccurate, and largely unreadable ‘rough translations’ that result when context is not specified and texts are not pre-or postedited. Such rough translations have proven economical in helping users to quickly categorize large numbers of foreign texts as either not useful or deserving of thorough translation.
Inexpensive translation software for personal computers, or freeware available on the Web, is widely used for obtaining the gist of e-mail, Web pages, and on-line discussion groups. These systems, often lacking large lexicons or phrase dictionaries, provide an even less exact gist than commercial systems. An example: French: Il fait vraiment beau! English: He does indeed beauteous! Correct translation: The weather is really nice! (Fortune City web site) But while many users are disappointed at the poor quality of these ‘quick and dirty’ translations, others are satisfied to have even an approximate idea of the contents of the text.
As always, the market is driven by demand. Currently, machine translation systems into and from English are available for a very large number of the world’s languages. Translation systems into and from the major European languages (French, German, Italian, and Spanish) are available for a smaller number of world languages, while dramatically smaller numbers of systems are available to translate nonmajor languages into other nonmajor languages.
Spoken language, rather than written, is the focus of much current research. The Verbmobil project of the German Ministry of Research and Technology, for example, aims to prototype an on-the-spot oral translation of business negotiations between German and Japanese speakers. The problems of speech recognition and speech generation, however, are even greater than those involved in written translation. Such projects always aim at clearly defined topic areas and types of users, rather than at unrestricted use, but even with this limitation, it is not foreseen that large systems for oral translation will be available for several decades.
Other areas of current research include the development of desktop systems for the ‘interactive’ composition of specialized documents (for example, business letters) in a target language by a human who does not know that language. The system generates the text by a process of asking the user more and more detailed questions about what is meant, and choosing from its inventory of phrases and structure frameworks.
Machine translation continues to be a lively field for research and development. Although completely automated high-quality translation will probably not be achieved in the near future, more user-friendly, inexpensive systems will continue to be developed, particularly those aimed at special domains and for special purposes.




http://%20www.fortunecity.com/business/reception/19/mtex.htm






بـــــــــــــــــــالتوفيق ..

[maziona]

لحظه لحظه شكلي فهمت غلط ..

الحين القطعه هذي مابتجيبها في الاختبار صح ؟ و بتحط لنا اسئله نجاوبها بس صح ؟؟؟؟

[ليان محمد]

بنات ايش نذاكر بالترجمه غير القطعه الي نزلتها من وين بتذاكرون الكلمات

[Diamond brooch]

بناااااااااااااات

شرايكم كل وحده
اذا حست في سؤال مهم بالقطعه
تحطه هنا
علشان نجمع الاسئله
ونذكر بعض
يمكن مو كل شخص ينتبه له ..

وان شاء الله خير ..
..

[maziona]

ماحذاكر كلمات الدكشنري يكفي

بنات الحين القطعه متى قالت الدكتوره انها معانا؟ اففففففف ياربي لازم نذاكر يعني

[ليان محمد]

بنات الي قرت القطعه مشين ترنزليشن تقول لنا ايش النقاط المهمه فيها بليــــــــــز

[ليان محمد]

بنات ترى على فكره القطعه عليها فايف مارك بس
بس اي نووو فايف مارك تفرق انشالله ماتكون صعبه انا ادري شطاري عليها حاطه قطعه ليه كمبريهانشن

[karezma]

بنات بس سؤال اليوم امتحان الشعر بالصح والخطأ , كان مكتوب نصحح الخطأ !!!