Ressources numériques en sciences humaines et sociales OpenEdition Nos plateformes OpenEdition Books OpenEdition Journals Hypothèses Calenda Bibliothèques OpenEdition Freemium Suivez-nous
Illustration of an ouroboros 1

Corpus linguistics in the LLM era – the changing nature of language data

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.

landing page to chisq-fisher-viz 0

Empowering budding corpus linguists with a custom shiny app

As corpus linguists, we often grapple with the question of how to empower students who may not have extensive coding skills to design tools that fit their research needs. With this in mind, I developed a modest yet very helpful Shiny app that simplifies running [latex]\chi^2[/latex] and Fisher’s exact tests for independence while offering access to the underlying code. This blog post explores how the app was built, its assets and limitations, and reflects on pedagogical goals.

1

Manipulate data with dplyr

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().

0

Tidy corpus linguistics data with tidyr

The tidyr package is part of the tidyverse. As its name indicates, it is meant to help you create tidy data or tidy messy data according to the tidy data principles: each variable forms a column; each observation forms a row; each type of observational unit forms a table. This post illustrates how to tidy a data set in R using two tidyr functions: pivot_longer() and separate().