We will offer, over the next years, courses that cover several topics from the domains of computational linguistics and natural language processing.

We start in the summer term 2024 to teach Information Retrieval and Text Mining. This lecture will cover the basics ­– how to represent documents as data, how to compare them, how to find them with queries or in other ways, how to classify them in predefined categories, and how to group them automatically. We discuss traditional machine learning methods and traditional distributional methods of representations.

We plan then to offer, in the winter term 2024/2025, a class on Natural Language Understanding. In this class, we will discuss ways to represent the meaning of words and phrases, with lexical and distributional semantics. After that, we talk about various tasks in NLU, including information extraction, named entity recognition, relation detection, entity linking, emotion and sentiment analysis, semantic role labeling, and natural language inference. From the methodological perspective, we will cover traditional machine learning and rule-based methods, and give pointers to state-of-the-art approaches.

In summer term 2025, we will then offer a class on Deep Learning for NLP, where in the first half, we will discuss, in a lecture, the most recent methods to process text. After that, we will offer small projects the students can work on to gain first-hand experiences.

Each of these classes can be taken separately and independently, but we recommend to do them all, if you want to specialize in NLP. Note that other chairs at the university also offer related and relevant topics on machine/deep learning or dialogue systems and language generation.

In addition to this fundamental offers, we offer specialized courses, lectures, and seminars. This will develop over time while we hire more people in the group. We start these more specialized courses with Emotion Analysis in the summer 2024. Here, we will have 8 lectures and 4 exercise sessions, which are small projects in which the students do small tasks related to the topic.

If you have specific ideas that you would like to learn about, please feel free to contact us.