Natural Language Processing

The Natural Language Processing (NLP) working group focuses on understanding human language with computers, and we develop methods for extracting knowledge from text with statistical methods. One of our main interests are deep learning methods, and we combine them with linguistic knowledge and knowledge about the world (which can be expressed in knowledge graphs).

Team

Benjamin Roth (Google Scholar) (head of NLP group)
Andreas Baumann (senior scientist)
Nurcan Cakan (administrative assistant)
Erion Çano (postdoctoral researcher)
Vasiliki Kougia (doctoral researcher)
Pedro Henrique Luz de Araujo (doctoral researcher)
Luisa März (doctoral researcher)
Johannes Pulkrab (student research assistant)
Anastasiia Sedova (doctoral researcher)
Andreas Stephan (doctoral researcher)
Lukas Thoma (doctoral researcher and technician)
Xia Yuxi (doctoral researcher)

Students: How to do a project or a thesis with us?

The NLP group offers practicals (Praktikum 1, Praktikum 2, Data Science Project, and others) and thesis topics (Bachelor, Master) for several study programs (Computer Science, Data Science, Digital Humanities, and others).

Please contact us as early as possible, if you think you might want to do a project or thesis in the area of Machine Learning for NLP or Knowledge Graphs. (The earlier, the better the topical fit. E.g., you could contact us even at the beginning of your Master studies about a potential thesis, without any commitment, of course.)

The best way to contact us is to join one of my (Benjamin Roth) virtual coffee sessions (link), Tuesdays, 2pm.

You can also contact us by email (benjamin.roth -at- univie.ac.at), please include the following information:

up-to-date transcript of records
Whether you
- are generally interested in Machine Learning for NLP and Knowledge Graphs (no particular topic in mind)
- or, have own ideas for a topic, or an interest in a specific problem in ML / NLP / KGs
- or, are interested in one of the topics from the list in Moodle. To see the topics, you need to be logged in to Moodle with your u:account, and add yourself to the linked Moodle course.