This page is under construction — materials for the course I am teaching this year at the European Summer School in Logic, Language, and Information will be uploaded here shortly.
Word embeddings (also known as distributional semantic models or vector space models) have been proved as an invaluable tool in natural language processing for modeling word meaning. Beyond their success in NLP, these models are increasingly used in theoretical linguistics for their potential to support large-scale, quantitative investigations of semantic phenomena (Lenci 2018; Boleda 2020; Wauquier 2022 for reviews).
In this course, we will explore the application of DSMs to the study of the semantics of morphological processes. We will address a range of distributional measures that can be used to assess semantic properties of affixes, including transparency, affix similarity and competition, morphological regularity, polysemy of derived forms, and affix polyfunctionality. Through selected case studies, it will be shown how these computational methods contribute to our theoretical understanding of morphological processes. The course will be hands-on: students will learn how to derive morphological representations and how to compute different measures in python.
Dates: 10–14 August 2026, 9:00–10:30 a.m.
Venue: Faculty of Civil Engineering, Czech Technical University, Thákurova 2077/7, 166 29 Prague 6
Room: C-217
Slides - Introduction to the course and to distributional models
Slides - Affix vector representations and compositional models
Slides - Many-to-many relation between forms and meanings
Slides - Measuring semantic properties of morphological processes