MEDAL Methods workshop: Modeling lexical processing
The first part consists of a general introduction to the 'Discriminative Lexicon Model' (DLM), a cognitive computational model of the mental lexicon that Harald has been developing over the last 10 years with generous support from the Alexander von Humboldt Foundation and the European Research Council.
The second part of the workshop will take a hands-on approach. Harald will introduce participants to the software package JudiLing that Maria Heitmeier, Yu-Ying Chuang, and himself have been developing to facilitate setting up instances of DLM models. He will then provide the worked examples of how the software can be used to set up computational models for understanding and producing Estonian nouns and how measures from these models can be harvested to predict the acoustic durations of Estonian nouns and word naming latencies for these nouns, using data collected in collaboration with Arvi Tavast and Kaidi Loo.
Where? This is a hybrid event that will take place both online on zoom and at the Insitute of Estonian and General Linguistics in Tartu in room 114. You will get the zoom link after registration.
09:00-11:00 Introductory lecture on the JudiLing package
11:00-13:00 Lunch break
13:00-15:00 Hands-on practical examples
About the instructor
Harald Baayen is a Professor of Quantitative Linguistics in Eberhard Karls Universität in Tübingen, Germany. Learn more about his work here.
This is a workshop organised by Methodological Excellence in Data-Driven Approaches to Linguistics (MEDAL) is an international consortium initiated by the University of Tartu in Estonia, in collaboration with the Max Planck Institute for Psycholinguistics and Radboud University in the Netherlands and the University of Birmingham in the United Kingdom. Financed by the EU Horizon Europe programme (101079429) and UK Research and Innovation organisation (101079429). MEDAL’s mission is to build expertise in data-driven linguistics methodology among early-career researchers. Read more about MEDAL here.