The role of machine translators in perception the multiple meaning words in the context | Статья в журнале «Молодой ученый»

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Авторы: ,

Рубрика: Филология, лингвистика

Опубликовано в Молодой учёный №7 (193) февраль 2018 г.

Дата публикации: 20.02.2018

Статья просмотрена: 6 раз

Библиографическое описание:

Кодирова, Р. Т. The role of machine translators in perception the multiple meaning words in the context / Р. Т. Кодирова, Ф. Н. Умарова. — Текст : непосредственный // Молодой ученый. — 2018. — № 7 (193). — С. 205-207. — URL: https://moluch.ru/archive/193/48397/ (дата обращения: 30.05.2020).



The following article is devoted to one of the main problems of semasiology to the polysemy and the role of electronic translators in perception the multilingual words in the context.

Nowadays there many types of electronic machines or translators, which are used by many students while learning English. Surely, they can be helpful in any cases, especially while working with texts on specialty. Nevertheless, the machinery translations need the correction, because they give only inaccurate and surface translation, which is not correct. Especially, it is difficult while translating polysemantic words, where machine can choose the different meaning of the word, which doesn’t match the context.

Translating polysemantic words with the help of electronic translator occupies an important place in the automatic processing of the texts. Lexical polysemy is understood to be the words of two or more bills of equivalents exchange, i.e. translation into another language, which in this case acts as a reference. Current research of polysemantic words within systems and machine translation algorithms shows that dictionary set of meanings often differ considerably from the context not only of the lexical meaning, but also on the frequency of use. So, a renowned expert on machine translation Y. Tsujii in his work mentioned, that, “… the lexical polysemy, led data illustrating the distribution of transfers in dictionaries and texts for Japanese-English language pair. This data shows that contextual meanings often do not correspond to words in the dictionary and lexical polysemy is very common even in very narrow subjects.” [6]

Contextual dictionaries and other means of resolving the ambiguity of lexical context currently have enough widespread, since other theoretically conceivable ways to resolve ambiguity, such as the resolution of ambiguity in the intersection of headings the thesaurus or syntactic classes of words, have not received the application due to its impracticality and the fact that they don't give a definite resolution of ambiguity, but merely restricted it. These methods require too many large and bulky thesauri or classifying groups of words, which are cumbersome and do not provide acceptable practical results. It is well known, for example, of the laws of logic, that the more specific concept, the more abstract characters need to be encoded. This pattern clearly traced by P. N. Denisov for learning vocabulary [1]. Consequently, the main means of resolving lexical ambiguity is determination of contextual attributes, searches the text of a translation of the words of the lexical, syntactic and morphological determinants [3].

Recent work in the field of computer linguistics pays great attention to regularities of explication vocabulary, syntax and semantics in specific sublanguage. Machine translation systems, which fit to practice as, for example, Chinese-English machine translation [2] by V. M. Zelko, sublanguage-oriented and micro-sublanguage with their lexical, structural, syntactical and semantic features. Low quality, given the practical machine translation systems by Y. Tsujii attributed the prevalence of the myth of the compositionality of translation. It has understood the idea of the logical framework to translate as songs or the amount of its separate parts, in a more general sense, as the generating of large units of the small ones. It shows that the translation does not have this property and of the correct translations of some parts of the text could not be automatically getting the correct translation of the text as a whole. Therefore, the compositionality of translation is just a myth. Without going into the theoretical discussion of this issue, it should be noted that his decision to Wed the exact accounting of specific features of substantive field and language specific sublanguage composition, i.e. the areas are known to be significantly smaller than the natural language in General. For such linguistic communities it becomes possible to determine the meaning of separate language units in such a way that their combination or linear combination is to some extent not contrary to the integrity of the text itself.

Recently, the idea of a contextual dictionary, first was put forward by G. Marchuk in 1976 for example contextual machine translation dictionary polysemantic words from English into Russian language in machine translation system AMPAR [4] has been fruitful in a number of studies on terminography. So, in the A. l. Semionov [5] contextual dictionary updated interpretation and usage contexts in the texts, contains a specific sub-language-marketing term in English-Russian bilingual situation. This use of contextual dictionary enables you to manage multilingual terminology databases for broad subject areas. The definition of the term in contexts its application gives ample opportunities for a more exact description of sublanguage and micro-sublanguage variety of subject areas, from very narrow to wide and developing.

As it is shown in the work of G. Marchuk [4], the contemporary usage of contextual dictionary gives the opportunity not only to describe specifically the vocabulary in subject areas, but also to establish the limits and general trends of diachronic development of individual lexical units. However, the main and the most important result of these dictionaries is the effective of solution of lexical ambiguity. Like many modern researches, namely lexical meanings of words have the main part of meaningful semantic information and text within the system of the discourse of the subject area.

References:

  1. Denisov P. N. Lexics of the Russian language and principles of vocabulary description (Лексика русского языка и принципы ее описания). — M.: Russkiy yazik, 1993.
  2. Zelko V. M. Problems of the development of linguistic support of Chinese-English machine information translation (Проблемы разработки лингвистического обеспечения системы Китайско-Русского информационного машинного перевода): Dis. -М.: Institute of Linguistics AS, 1991.
  3. Marchuk Y. N. Contextual machine translation of English-Russian dictionary of polysemantic words (Контекстологический словарь для машинного перевода многозначных слов с английского языка на русский). -M.: VCP, 1976.
  4. Marchuk Yu.N. Fundamentals of computer linguistics (Основы компьютерной лингвистики). -M.: Singal, 1999.
  5. Semenov A. l. Contextual dictionary of basic terms of marketing (Контекстологический словарь основных терминов маркетинга). -M.: VCP, 1994.
  6. Tsujii J. Machine translation: Productivity and Conventionality of Language // Current Issues in Linguistic Theory. Benjamin’s Publ. Co., Amsterdam/Philadelphia, 1997.
Основные термины (генерируются автоматически): VCP, AMPAR.


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