If you’re already tired of hearing about AI, brace yourself: it is not going away. That is why it is crucial to study AI language, from the early days of AI Dungeon (circa 2019), which gave a broad public its first taste of large language models, to the present (and beyond). In this talk, I present research on AI language using corpus- and psycholinguistic methods. Firstly, a live demonstration (or its screenshots alternative) of new publicly accessible AI-corpora, AI-Brown and AI-Koditex, will take place. Then, experiments on on AI-generated texts (including poetry), analyses of stylistic variability, and a study of AI-generated images will be presented. The goal of this talk is to offer a concise overview of recent work on large language models conducted at the Czech National Corpus. Let’s study AI before it studies us.
Comparing quantitative morphological features of languages: a study on annotated multi-parallel texts
Semantic networks for children with typical acquisition and specific language impairment
Tomáš Savčenko (OAJD)
I am preparing a study on semantic networks based on word vectors trained on the Clinical English Gillam corpus (Gillam & Pearson 2004) containing narratives of children with typical language development and specific language impairment (SLI). The aim is to analyse the structure of those semantic networks at different stages of acquisition with the hypothesis that a 'small-world structure', characterized by prominent hub words with many connections and local clusters of closely related words, will be found in typically developing children while a network with less dominant hubs and more evenly linked nodes will be found for children with SLI. Small-world network allows, in theory, effective search strategies in local clusters as well as across distant domains via the hub nodes (Watts & Strogatz 1998; Steyvers & Tenenbaum 2005) which is why I assume that its disruption should occur in SLI. Special focus will lie on whether this network measure would be able to distinguish typical and SLI children with similar mean length of utterance in which case this network measure would outperform a traditional psycholinguistic measure used to diagnose SLI (Rice et al. 2010).
