My research is in the area of natural language processing (NLP). I combine concepts and methods from theoretical computer science and machine learning to design new algorithms for natural language understanding, and apply these algorithms to practical problems in human language technology and text mining.

From Text to Meaning

Building computers that understand human language is one of the central goals of artificial intelligence. A key technology in language understanding is semantic parsing: the automatic mapping of a sentence into a formal representation of its meaning, such as a search query, robot command, or logic formula. Together with my coauthors, I have
developed fundamental algorithms for parsing to a widely-used type of meaning representation called dependency graphs, and contributed to a better understanding of the neural network architectures that define the state of the art for this task. We have compiled benchmark data sets for the development and evaluation of dependency parsers, and coordinated community-building efforts aimed at the comparison of different parsing systems and meaning representations.

Understanding Representations

A recent breakthrough on the way towards natural language understanding is the development of deep neural network architectures that learn contextualized representations of language, such as BERT and GPT-3. While this has substantially advanced the state of the art in natural language processing for a wide range of tasks, our understanding of the learned representations and our repertoire of techniques for integrating them with other knowledge sources and reasoning facilities remain severely limited. In my current research project – a collaboration with Chalmers University of Technology and the company Recorded Future – my group develops new methods for the interpretation, grounding, and integration of contextualized representations of language.


I am passionate about teaching and working with students. My main driving force as a researcher is the creative energy that I find in making complicated matters simple, and the very same energy also motivates me as a teacher. Today, most of my teaching is linked to my research in natural language processing and machine learning, but I have taught courses and supervised students in many different areas of computer science, and at all levels of university education. I am involved in several degree programmes at the Faculty of Science and Engineering and the Faculty of Arts and Sciences, and act as the examiner for the following courses:

Language Technology 

Natural Language Processing

Text Mining

Deep Learning for Natural Language Processing

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Robin Kurtz, Stephan Oepen, Marco Kuhlmann (2020) End-to-End Negation Resolution as Graph Parsing Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies , s. 14-24


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