filnaukiФилологические науки в МГИМОMGIMO Journal of Language Research and Teaching2410-24232782-3717МГИМО МИД Россииfilnauki-511Research ArticleМетодика преподавания родных и иностранных языковINNOVATIVE METHODS AND COMPETENCE APPROACH IN TEACHING FOREIGN LANGUAGESБазовые методики анализа языковых картин политики с помощью языка программирования Python и библиотеки NLTK (на материалах корпусов британского парламентского дискурса)Getting a handle on a Hansard with Python and NLTK, or how to tame the linguistic picture of British politics with NLPhttps://orcid.org/0000-0002-8893-5083ГагаринС. Н.GagarinS. N.

Гагарин Сергей Николаевич − кандидат филологических наук, старший преподаватель кафедры английского языка №1 

119454, Москва, пр. Вернадского, 76

Sergey N. Gagarin, Candidate of Philology, is Senior Lecturer of the English Language Department №1

76, Prospect Vernadskogo, Moscow, 119454

lexicogr@mail.ruМосковский государственный институт международных отношений (университет) МИД РоссииРоссияMGIMO UNIVERSITYRussian Federation202421062024102125140Copyright © Гагарин С.Н., 20242024Гагарин С.Н.Gagarin S.N.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://philnauki.mgimo.ru/jour/article/view/511

В рамках данной статьи предлагается один из возможных вариантов оптимального набора базовых методик, необходимых для изучения языковых картин мира на материалах крупных текстовых корпусов с использованием сочетания языка высокоуровневого языка программирования Python и библиотеки NLTK (Natural Language Toolkit). Необходимость выделения и конкретизации означенного методологического инструментария проистекает из характера тех вызовов, которые стоят перед современной когнитивной лингвистикой и лексикологией в сфере анализа больших неструктурированных данных. Работоспособность и практическая ценность предлагаемых методик демонстрируется на примере составленных автором сплошных параллельных диахронических корпусов дискурса обеих палат британского парламента за период с 2006 по 2023 гг., совокупный объём которых превышает треть миллиарда токенов. Набор предлагаемых методик включает в себя инструменты анализа базовых параметров вокабуляра, инструменты извлечения целевого вокабуляра, а также обработки и визуализации его частотных параметров и сочетаемости. Целесообразность овладения предлагаемыми и систематизируемыми в рамках данной статьи методиками автоматического анализа текста обосновывается с позиции их необходимости как компетентностного минимума в области компьютерных технологий, который способен значительно повысить уровень лингвистических исследований и их научную конкурентоспособность. Автор приходит к выводу о том, что в силу объективно сложившихся условий современному отечественному исследователю языковых картин мира с высокой долей вероятности придётся включить в свой прикладной инструментарий сочетание языка программирования Python и библиотеки NLTK. Предлагаемые в данной статье методики делают возможным гибкое формирование исследовательских протоколов с учётом широкого разнообразия возможных приоритетов. В качестве главного преимущества предлагаемого автором набора методов машинной обработки и количественного анализа текста видится возможность использования практических навыков, полученных в результате её освоения как комфортной компетентностной основы для последующей интеграции овладевшего ими лингвиста в сообщество исследователей наиболее высокотехнологичных и наиболее актуальных на сегодняшний день направлений науки о языке.

The article proposes an optimised starter’s set of basic Python and NLTK (Natural Language Toolkit) methods that are essential in the analysis of massive textual corpora conducted as part of research investigating linguistic images of the world. The need to specify and detail these applied techniques stems from the nature and scope of the inexorable challenges confronted by contemporary cognitive linguistics and lexicology in the realm of unstructured big data analysis. Their viability and practical value are demonstrated in a series of illustrative examples where they are applied to the processing of continuous parallel diachronic corpora of Hansard that capture the discourse of both chambers of the British parliament produced in the years 2006-2023 and jointly amounting to over a third of a billion tokens.

The article suggests that the methods it outlines and classifies can be seen as forming an indispensable minimum of IT competences that is capable of delivering a substantial boost to the level of research both as regards its overall quality and its competitive edge. The proposed toolkit includes an essential set of instruments for target vocabulary processing as well as for the assessment and visualisation of word and phrase frequency and collocation.

The author presumes that, urged by the need to keep abreast of prevailing trends, the contemporary Russian researcher of linguistic images of the world is highly likely to find themselves compelled at some point to embrace the quantitative analysis methods made possible by combining Python and NLTK. As part of its substantial and varied range of benefits, the latter would arguably help them design and customise research protocols, adapting them with ease and versatility. Lastly and most importantly, the author suggests that Python and NLTK skills may serve as a comfortable gateway towards eventually upgrading one’s linguistic research to cutting-edge global standards of technological sophistication and marketability.

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The authors declare that there are no conflicts of interest present.