The role of computer linguistics in the field of philology | Статья в журнале «Молодой ученый»

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Рубрика: Филология, лингвистика

Опубликовано в Молодой учёный №19 (309) май 2020 г.

Дата публикации: 11.05.2020

Статья просмотрена: 157 раз

Библиографическое описание:

Мавлянова, Т. Б. The role of computer linguistics in the field of philology / Т. Б. Мавлянова, Ш. Н. Ахтамова. — Текст : непосредственный // Молодой ученый. — 2020. — № 19 (309). — С. 538-540. — URL: https://moluch.ru/archive/309/69748/ (дата обращения: 16.12.2024).



This article highlights the role of modern information technology in philology and translation today. It should also be noted that the role of computer technology in linguistics is invaluable. This can be seen in the example of machine translation, which is widely used today in computer linguistics. This article describes the practical significance of computer linguistics in the field of philology and its possibilities.

Key words: direct translation system, machine linguistics, machine translation, computer linguistics, interlingual machine translation, dictionary-based machine translation, Corpus based machine translation HAMT.

Статья посвящается роли современной компьютерной технологии в филологических дисциплинах. Необходимо отметить, что роль компьютерной технологии значима в лингвистике. Это можно проследить на примере активного развития машинного перевода в компьютерной лингвистике. В статье раскрыта практическая значимость компьютерной лингвистики в филологических дисциплинах.

Ключевые слова: буквальный перевод, машинная лингвистика, машинный перевод, компьютерная лингвистика, межязыковой машинный перевод, словарный машинный перевод, корпусная база машинного перевода.

It is known that the science of linguistics was formed in the XIX (1816) century as an independent science. Since then, it has been developing in various aspects and directions. In recent years, as in all disciplines, linguistics has developed rapidly at the «intersection» of the two disciplines. Such disciplines include sociolinguistics (sociology and linguistics), psycholinguistics (psychology and linguistics), ethnolinguistics (ethnography and linguistics), neurolinguistics (neurology and linguistics), mathematical linguistics and computer linguistics. This can be observed in other disciplines as well: biochemistry, astrophysics, mathematical physics, mathematical logic. It should be considered as the interaction and integration of several disciplines in the system of sciences. From the 50s of the XX century in linguistics the basics of computer linguistics began to use the terms «machine translation», «machine linguistics» [4]. The great invention of this century was the proof that computer technology had also entered linguistics. Machine translation or automatic translation means translating text from one language to another in a short time by computer. The founders of machine translation were representatives of cybernetics and mathematics, and later linguists began to take an active part in this work. Thus, the ideas of machine translation have played an important role in the development of theoretical and practical linguistics around the world. In parallel with this direction, the theory of formal grammar emerged, with a focus on creating a model of language and its individual aspects. These aspects of language were developed in the science of mathematical linguistics, which, in turn, laid the foundation for the emergence of the science of computer linguistics. So, on this basis, a new direction of linguistics — computer linguistics and a number of theoretical and practical directions of linguistics has emerged.

The word machine in the combination of machine translation is considered in the sense of artificial intelligence as the executor of human-specific features: leakage, verification, translation and editing processes. The term computer is derived from the English word compute. Although computer translation is modern, it is not essentially accurate. In some places, an automatic translation combination is also used. However, this combination is used in a narrow sense relative to the machine, that is, only in the sense of program execution. Since machine translation is a broad concept, there are now four types of cake:

1) machine-assisted human translation HAMT — the use of computer electronic dictionaries and instructions included in human translation of text;

2) computer-assisted translation (CAT) — computer-assisted translation of the text and the separation of the basic concepts understood in it;

3) assignment of the task of editing by human resources to the translation carried out by the computer software of human-assisted machine translation (HAMT) with the help of the editor;

4) Fully automatic machine translation (FAMT) — perform both translation and editing of the text using the translator program. In the information age, many machine translation systems have been created.

These machine translation systems are characterized by certain aspects:

1) the presence of languages: a) bilingual; b) multilingual;

2) the amount of the chosen topic: a) related to a particular style and field; b) belongs to one style and many areas;

3) related to different styles and several areas. John Hutchins distinguishes machine translation systems as follows [5]:

  1. Rule-based MT-MT-Rule-Based MT:

1) transfer system (transfer-based machine translation);

2) interlingual machine translation;

3) dictionary-based machine translation;

  1. Corpus based machine translation — Corpus based MT:

1) statistics-based MT (statistics-based);

2) sample-based MT (example-based);

  1. Hybrid machine translation (HMT). A hybrid machine translation is a system that uses translation technology based on rules and statistics. Several machine translation companies (Asia Online, LinguaSys, Systran, PangeaMT, UPV, Logos) use this system.

Statistical machine translation technology is a method of machine translation using large volumes of comparative language pairs and text samples (corpus). The first idea of statistical machine translation was created in 1949 by Warren Weaver based on Claude Shannon's data theory. According to this theory, the system was reorganized in 1991 by IBM researcher Thomas J. Watson. Statistical translation models were originally based on words (Models 1–5 from IBM Hidden, Stefan Vogel from Markov, and Franz Joseph Okdan, but later phrase-based models also emerged [6, 7]. IBM created the IBM Model 1–5 using the statistical machine translation method. Google has also started using this method. Today, the same method is used effectively in the following systems:

1) Giza ++;

2) Moses;

3) Pharaoh;

4) Rewrite;

5) BLEU scoring tool.

Creating a similar database in Uzbek is the most urgent task today. Another trend in Russian computer linguistics is the automatic editing of texts. Among the scientists who have conducted research in this area are R. R. Kotov, V. E. Berzon, VG Britvin, I. A. M. Elchuk, L. I. Belyayeva, V. A. Chijakovsky, G. G. Belonogov, I. S. Duganova, A. B. Kuznetsov. The following researches of the mentioned scientists can be distinguished: G. G. Belonogov and G. G. Kotov's «Automated information-search systems» (1968), G. G. Kotov's «Linguistic aspects of automated control systems» (1977).

Computer linguistics also plays an important role in the specific features of a work of art and their translation. While literature reflects the life of a nation, it is only natural that its national and universal aspects of social life should be artistically expressed. As the German literary poet and scholar Johannes Robert Beher put it, the national content of literature is undoubtedly understood to be related to the historical tasks of literature facing the nation, the national interests of the people. In general, literature determines the life, profession, national character of the people. The national characteristics of a nation or people are not abstract concepts, but the product of a specific historical period, economic and social system. Nationality does not contradict nationalism, and universality does not deny nationality. When we read ideologically and artistically mature works, we cannot imagine the nationalism expressed in them in isolation from universalism, as well as nationalism (internationalism) from nationalism. Because in such works, nationalism and nationhood are closely intertwined and gain integrity. But nationalism, shrouded in a narrow shell of localism, cannot be a true artistic expression of national life: it cannot acquire universality. Therefore, it can be said that the more national a work of art is, the more national it is. Although East-West scholars have conducted many experiments and developed various rules on the methods of expressing nationality in translation, it is not always possible to give a clear indication in translating national concepts in a particular work. In the process of analysis of translations from modern Eastern languages into Western languages or vice versa, the problems facing the science of translation, the research and conclusions of a number of translation scholars in this regard are discussed.

However, other aspects of the issue should not be overlooked. Well-known Uzbek translator G '. According to Salomov, fine arts, music and fiction are inextricably linked, and «color» and «paint» are one of the elements that connect all three. The main difficulty in conveying national identity to other peoples is that peoples live far away from each other. In addition to the many similarities in the way of life of the peoples close to the territory, it is possible to feel the harmony in their views on art and literature.

References:

  1. Po'latov A., Muhametlova S. Kompyuter lingvistikasi. — Toshkent, 2007. — B.5–6.
  2. G'ofurov Ibrohim Tarjima nazariyasi: Oliy o'quv yurtlari uchun o'quv qo'lIanma II.G'ofurov, O,Mo'minov, N.Qambarov. — Toshkent: Tafakkur-Bo'stoni, 2012. 216 b.
  3. Abduraxmonova Nilufar Zaynobiddin Qizi Мashina tarjimasining lingvistik ta’minoti (monografiya). — Toshkent, 2018.
  4. Rahimov A. Kompyuter lingvistikasi asoslari. Т.: Akademnashr, 2011. - 160 b.
  5. Система машинного перевода «Кросслятор 2.0» и анализ ее функциональности для задачи трансляции знаний http://www.keldysh.ru/papers/2007/prep89/prep2007_89.html
  6. Koehn P., Och F.J., Marcu D. Statistical phrase based translation. In Proceedings of the Joint Conference on Human Language Technologies and the Annual Meeting of the North American Chapter of the Association of Computational Linguistics (HLT/NAACL) 2003;
  7. Chiang D. A Hierarchical Phrase-Based Model for Statistical Machine Translation. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05), 2005.
Основные термины (генерируются автоматически): IBM, HAMT, машинный перевод, компьютерная лингвистика, ACL, BLEU, CAT, FAMT, HLT, HMT.


Ключевые слова

direct translation system, machine linguistics, machine translation, computer linguistics, interlingual machine translation, dictionary-based machine translation, Corpus based machine translation HAMT

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