The tutorials will be on 28th January:
14:30 Looking at natural language through Python
Lecturer: Matthijs Westera
This tutorial could be your first exposure to the Python programming language and its Natural Language ToolKit (NLTK). There will be hands-on exercises and we’ll reflect on how you could use Python and NLTK for your research. No prior familiarity with programming required.
Natural Language Toolkit: https://www.nltk.org/ (especially https://www.nltk.org/book/)
16:00 Coffee break
16:30 Introduction to Distributional Semantics and Machine Learning
Lecturer: Gemma Boleda
In this tutorial, we will aim at giving attendants a basic understanding of techniques in Machine Learning and Distributional Semantics, and discuss the interface between these methods and theoretical linguistics. The tutorial will be based on a talk and hands-on practice, and will cover the following contents:
- Machine learning for Computational Linguistics.
- Distributional Semantics.
- Language models.
Boleda, G. Distributional Semantics and Linguistic Theory. Annual Review of Linguistics: Accepted. DOI: 10.1146/annurev-linguistics-011619-030303.
Cichy, R. M., & Kaiser, D. 2019. Deep neural networks as scientific models. Trends in cognitive sciences 23:4, 305-317.
Pater J. 2019. Generative linguistics and neural networks at 60: Foundation, friction, and fusion. Language 95, e41-e74.