Building a Shakespeare character network
Ten years ago I wrote a small Shiny app that drew a network of Macbeth’s characters. I showed it to colleagues working on Shakespeare, they liked it, I posted it, and then I left...
Ten years ago I wrote a small Shiny app that drew a network of Macbeth’s characters. I showed it to colleagues working on Shakespeare, they liked it, I posted it, and then I left...
Eight years ago I wrote a post called Word embeddings: the (very) basics. It apparently struck a chord, because people still find it, still link to it, and occasionally still email me about it....
In Corpus Linguistics and Statistics with R, I showed how to make a word cloud from a text file (Section 6.3). A word cloud is a user-friendly way of representing a frequency list graphically....
If you work with large annotated corpora, you will know the feeling. You have a research question. You know roughly what you are looking for: a construction, a collocation, a specific part-of-speech sequence. You...
A while back, I wrote about a Shiny app I built for my Master students at Université Bordeaux Montaigne, one that automates the choice between a χ² test and Fisher’s exact test of independence...
What happens when you ask fifteen Master’s students to annotate the pragmatic uses of a single adverb across eight thousand historical sentences, then train a neural network on their collective judgments? This post chronicles...
In a previous post, I argued that large language models were contaminating the linguistic ecosystem by producing a generic, formulaic style that spreads through the language like dye through water. A shorter article version of...
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.
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.
As a corpus linguist, I happen to teach my students how to use R for text analysis. However, due to the steep learning curve associated with R, I sometimes need to provide more accessible,...
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...
Each academic year, I lead my Master’s students in English linguistics on an intellectual journey that traces the evolution from traditional structuralist semantics to contemporary cognitive linguistics. A significant moment of this journey is...
As a follow-up to my previous post (POS-tagging in R with UDPipe), I explore the udpipe package further, focusing this time on dependency parsing. Dependency parsing is a process of analyzing the grammatical structure...
POS stands for “part-of-speech” (i.e. the grammatical nature of a word). POS tagging is the process of assigning a part-of-speech (such as noun, verb, adverb, adjective, determiner, etc.) to each word in a given...
In Corpus Linguistics and Statistics with R, I showed how to make a word cloud from a text file (Section 6.3). A word cloud is a user-friendly way of representing a frequency list graphically....