Étiqueté : corpus linguistics
The emergence of Large Language Models (LLMs) has brought both opportunities and challenges to the field of corpus linguistics. These AI systems generate vast amounts of language output that often appear natural, but is this output genuinely authentic? This raises important questions for corpus linguists about the nature of linguistic data and the methods used to study it.
In a previous post, I provided two frequency lists without revealing the scripts to make them. This post explains how to create word frequency lists (lemmatized and unlemmatized) from the BNC 2014 (spoken), using...
L’Ecole Doctorale 139 de l’Université Paris Nanterre organise un séminaire pluridisciplinaire thématique. Cette année (2020-2021), le thème imposé est “Preuves et présomptions”. Avec Sylvain Kahane, professeur en sciences du langage à l’Université Paris Nanterre,...
Multidimensional scaling (MDS) is a very popular multivariate exploratory approach because it is relatively old, versatile, and easy to understand and implement. It is used to visualize distances in multidimensional maps (in general: two-dimensional...
The dplyr package is based on a data manipulation ‘grammar’. This grammar provides a consistent set of ‘verbs’ that solve the most common data manipulation tasks. I illustrate five of these ‘verbs’: filter(), arrange(), select(), mutate(), and summarise().
This short post is the first of a series on network graphs for corpus linguistics. Because of the COVID19 pandemic, such graphs have been in the spotlight in the last few months for their...