Contrastive Research in Translation Study: Corpus Approach
Abstract
Elena M. Kovalenko (Rostov-on-Don, Russian Federation)
This article discusses some issues of the corpus approach application in the professional activities of a linguist-translator. It describes the collection of corpora of the English language, created by Mark Davis, Professor of Linguistics of Brigham Young University. The paper analyzes the potential of the Russian National Corpus, General Internet-Corpus of Russian (RNC, GICR) and one of the largest corpora created by Mark Davis - Corpus of WebBased Global English (GloWbE) in solving translational problems. It also considers the applications of monolingual corpora in conducting contrastive research in the field of translation studies: some lexical variations in different English dialects in the Corpus of Web-Based Global English, as well as their translation equivalents in the Russian corpora were studied; a comparative analysis of the obtained results for the addressed linguistic phenomenon was conducted. It is shown that such approach allows us to study the difference in the contexts of natural use not only in languages in general, but also in separate dialects of each language.
Key words: corpora, corpus linguistics, translation studies, collocations, contrastive corpus research, RNC, GICR, GloWbE.
DOI 10.23683/1995-0640-2017-3-56-65
References
Боровикова Е. Российские учёные разметят интернет [Электронный ресурс]. // STRF.ru – Наука и технологии России. 18.12.2012. URL: http://www. strf.ru/material.aspx?d_no=50859&CatalogId=21731#.VyjLjdKyNBc (дата обращения 14.03.2017).
Генеральный Интернет-Корпус Русского Языка (ГИКРЯ). О проекте. [Электронный ресурс]. URL: http://www.webcorpora.ru/ (дата обращения 14.03.2017).
Голов А. Н. Информационные технологии как инструмент перевода иноязычного технического текста // Наука и современность. 2011. № 11 C. 244–249. Захаров В. П., Богданова С. Ю. Корпусная лингвистика. Иркутск, 2011. 161 с.
Захаров В. П. Сочетаемость через призму корпусов //Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной Международной конференции «Диалог» (Москва, 27–30 мая 2015 г.). Вып. 14 (21): В 2 т. Т. 1. М.: Изд-во МГГУ, 2015. С. 667–682.
Ляшевская О. Н., Шаров С. А. Частотный словарь современного русского языка (на материалах Национального корпуса русского языка). М.: Азбуковник, 2009. 1112 с.
Митрофанова Т. А. Роль современных ИКТ в формировании профессиональной компетентности переводчиков // Современное языковое образование: инновации, проблемы, решения: сб. науч. тр. М.: Изд-во МГГУ им. М.А.Шолохова, 2014. с. 42 – 51.
Национальный корпус русского языка (НКРЯ). [Электронный ресурс]. URL: http://www.ruscorpora.ru/index.html (дата обращения 14.03.2017).
Потемкин С. Б. Проблемы разработки параллельного корпуса переводов русской классики // Научно-информационный журнал «Армия и общество». 2012. № 2 (30). С. 138 – 146.
British National Corpus (BYU-BNC). [Электронный ресурс]. URL: http:// corpus.byu.edu/bnc/ (дата обращения 12.03.2017).
Corpus of Contemporary American English (COCA). [Электронный ресурс]. URL: http://corpus.byu.edu/coca/ (дата обращения 12.03.2017).
Corpus of Global Web-Based English (GloWbE). [Электронный ресурс]. URL: http://corpus.byu.edu/glowbe/, http://corpus.byu.edu/glowbe/help/ dialects.asp (дата обращения 12.03.2017).
Corpus of Historical American English (COHA). [Электронный ресурс]. URL: http://corpus.byu.edu/coha/ (дата обращения 12.03.2017).
Kutuzov A., Andreev I. Texts in, Meaning out: Neural Language Models in Semantic Similarity Tasks for Russian, Computational Linguistics and Intellectual Technologies. Papers from the Annual International Conference (Dialogue’2015), Issue 14, vol. 2, of 2, Moscow, 2015, pp. 133 – 144.
Mikolov T., Le Q. V., Sutskever I. Exploiting similarities among languages for machine translation. Arxiv preprint, 2013. Available at http://arxiv.org/ abs/1309.4168 (дата обращения 14.03.2017).
RusVectōrēs. О проекте. [Электронный ресурс]. URL: http://ling.go.mail.ru/dsm/ru/about (дата обращения 14.03.2017).
References
Borovikova E. Rossiyskiye uchenyye razmetyat internet. STRF.ru – Nauka i tekhnologii Rossii. 18.12.2012. Avialable at: http://www.strf.ru/material.aspx?d_ no=50859&CatalogId=21731#.VyjLjdKyNBc (accessed 14.03.2017). (In Russian).
General’nyy Internet-Korpus Russkogo Yazyka (GIKRYa). O proyekte. Avialable at: http://www.webcorpora.ru/ (accessed 14.03.2017). (In Russian).
Golov A. N. Informacionnyye tehnologii kak instrument perevoda inoyazychnogo tekhnicheskogo teksta, Nauka i sovremennost’, 2011, no 11, pp. 244249. (In Russian).
Zakharov V. P., Bogdanova S. Yu. Korpusnaya lingvistika, Irkutsk, 2011, 161 p. (In Russian).
Zakharov V. P. Sochetaemost’ cherez prizmu korpusov. Komp’uternaya lingvistika i intellektual’nyye tekhnologii: po materialam ezhegodnoy Mezhdunarodnoy konferentsii «Dialog», 14 (21), Tom 1, Moscow, 2015, pp. 667-682. (In Russian).
Lyashevskaya O. N., Sharov S. A. Chastotnyy slovar’ sovremennogo russkogo yazyka (na materialakh Nacional’nogo korpusa russkogo yazyka), Moscow, 2009, 1112 p. (In Russian).
Mitrofanova T. A. Rol’ sovremennykh IKT v formirovanii professional’noy kompetentnosti perevodchikov, Sovremennoye yazykovoye obrazovaniye: innovatsii, problemy, resheniya, Sbornik nauchnykh trudov, Moscow, 2014, pp. 42-51. (In Russian).
Natsional’nyy korpus russkogo yazyka (NKRYa). Available at: http://www. ruscorpora.ru/index.html (accessed 14.03.2017). (In Russian).
Potemkin S. B. Problemy razrabotki parallel’nogo korpusa perevodov russkoy klassiki, Nauchno-informacionnyy zhurnal Armiya i obshchestvo, 2012, no 2 (30), pp. 138-146. (In Russian).
British National Corpus (BYU-BNC). Available at: http://corpus.byu.edu/ bnc/ (accessed 12.03.2017).
Corpus of Contemporary American English (COCA). Available at: http:// corpus.byu.edu/coca/ (accessed 12.03.2017).
Corpus of Global Web-Based English (GloWbE). Available at: http://corpus.byu.edu/glowbe/, http://corpus.byu.edu/glowbe/help/dialects.asp. (accessed 12.03.2017).
Corpus of Historical American English (COHA). Available at: http://corpus. byu.edu/coha/ (accessed 12.03.2017).
Kutuzov A., Andreev I. Texts in, Meaning out: Neural Language Models in Semantic Similarity Tasks for Russian, Computational Linguistics and Intellectual Technologies, Papers from the Annual International Conference (Dialogue’2015), Issue 14, vol. 2 of 2, Moscow, 2015, pp. 133-144.
Mikolov T., Le Q. V., Sutskever I. Exploiting similarities among languages for machine translation. Arxiv preprint, 2013. Available at: http://arxiv.org/ abs/1309.4168. (accessed 12.03.2017).
RusVectōrēs, O proyekte, Available at: http://ling.go.mail.ru/dsm/ru/about (accessed 14.03.2017). (In Russian).
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).