General and free elective courses

(15 credits with min. 5 credits from general courses)

General courses

• Methods in Corpus Linguistics and Experimental Linguistics (SEM 1)

This course introduces students to advanced corpus analysis techniques within cognitive and functional linguistics, emphasizing the study of language in real communicative contexts. Students will explore corpus-based methodologies to address theoretical linguistic questions. By the end of the course, students will have the skills to analyze corpus data, apply statistical techniques and present findings in a scientific paper.

• Language Variation and Change (SEM 2)

This course explores the relationship between language variation and the language system,

using examples from various languages to illustrate how variation reflects language change. Students will engage with theories, models, and methods for analyzing spatial and social variation, as well as diachronic change. The course has two main components: a theoretical section focused on seminal literature on language variation and change, and an empirical research project where students investigate a specific syntactic change utilizing a parsed corpus from languages such as English, French or Chinese. Practical sessions at the beginning of the course will provide hands-on experience working with these corpora. 

• Introduction to Psycholinguistics (SEM 1)

This course covers fundamental topics in psycholinguistics, focusing on the cognitive and neurobiological aspects of language. Key themes include the relationship between language and cognition, linguistic representation, and comprehension and production processes. Through lectures and exercises, the students will gain a foundational understanding of language processing in the mind and brain while engaging with research methods and empirical results in this domain.

• Advances in Psycholinguistics (SEM 2)

This course contributes to a deeper knowledge of psycholinguistics, with special attention to recent developments in the domain. Additionally, the course contributes to the strengthening of several scientific skills, including critical reflection about research, spoken and written presentation, and the ability to jointly take a stand on complex matters. The course zooms in on three timely and theoretically important themes that are treated in-depth (multiple classes, papers, presentations, and discussions) using a flipped classroom approach.

Free elective courses (SEM 1 or 2)

Besides the general courses listed above, students may enroll in other course(s) that align with the scientific profile they have selected. Eligible are: courses from master’s programs from a Belgian or foreign university as well as the remaining courses in the NLP specialisation of this program. Elective courses are subject to approval by the program committee.

Research project

This course (5 credits) is designed to deepen the students’ expertise in linguistics or literary studies. Students select a research topic, formulate an initial research question, and collaborate with a tutor, who may be their thesis supervisor or another qualified university staff member, to develop a comprehensive research project. The course involves independent work, which includes drafting a detailed research proposal and engaging in practical research activities.

NLP Specialisation 

20 credits

• Introduction to Language Processing with Python (sem1, w1-3)

This course offers an introduction to programming, focusing on automatic text processing. No prior programming knowledge is required. Python, a widely used programming language for natural language processing, serves as the primary tool.

• NLP and linguistic analysis (Sem 1, W4-12)

This course provides a comprehensive introduction to the fundamentals of natural language processing. Students will explore techniques for analyzing text at multiple levels (morphological, syntactic, semantic, and discourse) to develop a deeper understanding of computational language understanding and generation. The course covers key theoretical concepts in language technology, such as language models and dynamic programming, alongside practical tasks like part-of-speech tagging, lemmatization, parsing, and named entity recognition.

• Introduction to machine learning and feature engineering for NLP (sem 1, w4-12)

Machine learning enables computers to learn from and make predictions based on data without being explicitly programmed. This course provides a comprehensive introduction to machine learning techniques and the role of feature engineering in building effective models. Students will explore fundamental machine learning principles, learn to extract meaningful features from data, and generate new ones through analysis. Hands-on exercises and real-world applications will help them develop practical skills to implement these techniques and improve predictive accuracy and model interpretability.

• Neural networks and NLP applications (sem 2, w1-9)

In recent years, artificial neural networks have excelled in a wide range of natural language processing tasks. Inspired by the human brain, these algorithms process input through interconnected neurons, passing through hidden layers to generate output. This course offers a comprehensive introduction to neural networks, covering key architectures, including the innovative  transformer model and its components. The course also explores pre-trained large language models (LLMs) and fine-tuning strategies for specific tasks. Through a blend of theory and hands-on practice, students will actively develop and experiment with Python code in each session.

• Ethics for human-centered AI (sem 1, w4-12)

This course raises awareness of the risks involved in building and implementing AI-systems and explores what ‘human-centered’ AI would look like. Each session addresses concerns raised by AI ethicists, such as environmental and financial costs, bias, stereotyping, data privacy, and copyright, along with existing or needed mitigation strategies. Grounded in current news, debates, and recent academic research, the course emphasizes fairness, accountability and transparency in AI development. It also explores key challenges in human-centered design of AI products.

Master’s dissertation

The dissertation (20 credits) is a crucial requirement for obtaining a Master’s degree, serving as an original research project that enhances students’ research skills and capacities. Each student selects a specific topic and receives guidance from a supervisor throughout the process. The dissertation can be formatted as a traditional master’s paper of approximately 25,000 words or as a scientific article intended for submission to a scholarly journal, with the selection of appropriate journals decided in collaboration with the supervisor. Additionally, the article must include appendices that contain data, transcriptions, and analyses to ensure a thorough evaluation of the study’s scientific quality. The completed dissertation will be assessed by a committee consisting of the supervisor and two reviewers, culminating in an oral defence where the candidate discusses their work.