General description
Natural Language Processing (NLP) is a discipline that focuses on the interaction between linguistics, computer science, and artificial intelligence. The research domain is particularly concerned with learning machines to read, understand and derive meaning from natural language. The participants of this course will learn important theoretical concepts in NLP (e.g. language models, machine learning) and specific applications (e.g. sentiment analysis). Furthermore, they will acquire basic programming skills in Python by learning fundamental concepts such as variables, operators, conditions, loops and using libraries that are specifically developed for text analysis.
The sessions are organized as follows: NLP fundamentals will be taught in the morning session, which is then followed by a Python course in the afternoon. Both sessions are hands-on; participants will digest the knowledge by exercising on their laptop.
Target audience
PhD students with a background in (applied) linguistics, translation or interpreting studies and an interest in digital techniques.
Course prerequisites
No prior knowledge about programming or NLP is required.
Course materials
Copies of slides and programming exercises will be provided. The participants are free to bring their own laptops (with Internet connection) or to use PCs provided in the classroom (using guest accounts provided by the UGent).
Teacher bio
Prof. Dr. Véronique Hoste is senior full professor of Computational Linguistics at Ghent University and director of the LT³ language and Translation Technology Team (https://lt3.ugent.be). She holds a PhD in computational linguistics from the University of Antwerp on Optimization Issues in machine learning of coreference resolution (2005). Based on the conviction that shallow representations based on lexical information are not sufficient to model text understanding, she has performed research on coreference resolution, event detection etc., and in applications exploiting these deeper text representations, such as readability prediction, aspect-based sentiment analysis, irony detection, etc.
Prof. Dr. Arda Tezcan has a background in mathematics, artificial intelligence and holds a Ph.D. in translation studies. He currently works at the Language and Translation Technology Team (LT3) at Ghent University as an assistant professor in machine learning. His research interests include natural language processing, machine translation and human-machine interaction in the context of translation studies. He also teaches introductory courses on programming with Python and on translation technology.
Loic De Langhe is a Ph.D. student at the Language and Translation Technology Team (LT3) currently working on NLP applications for event coreference resolution. He holds degrees in Computational Linguistics and Artificial Intelligence from the universities of Antwerp and Leuven respectively. His research interests include transformer language models and deep neural models for NLP.
Dr. Cynthia Van Hee is a lecturer and post-doctoral researcher at the LT3 Language and Translation Technology Team at Ghent University, active in the field of computational linguistics and machine learning. She holds a PhD in linguistics on Automatic Irony Detection on Social Media. As a postdoc researcher, her main research focus lies on automatic (implicit) sentiment analysis. She teaches Audiovisual Language Techniques, Natural Language Processing, Project Management, Desktop Publishing, Subtitling and Dissertation.
Schedule
- Monday 15/07/2024, 9:00-10:30 & 11:00-12:30
- Tuesday 16/07/2024, 9:00-10:30 & 11:00-12:30
- Wednesday 17/07/2024, 9:00-10:30 & 11:00-12:30
- Thursday 16/07/2024, 9:00-10:30 & 11:00-12:30
- Friday 19/07/2024, 9:00-10:30 & 11:30-12:30