Getting a handle on a Hansard with Python and NLTK, or how to tame the linguistic picture of British politics with NLP-Reference-Cited by-同舟云学术

Getting a handle on a Hansard with Python and NLTK, or how to tame the linguistic picture of British politics with NLP

Published:2024-06-21 Issue:2 Volume:10 Page:125-140
ISSN:2782-3717
Container-title:Linguistics & Polyglot Studies
language:
Short-container-title:Linguistics & Polyglot Studies

Author:

Gagarin S. N.¹^ORCID

Affiliation:

1. MGIMO UNIVERSITY

Abstract

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.

Publisher

MGIMO University

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