CAROLL Group

Welcome to the website of University of Passau’s Natural Language Processing (CAROLL) Group! We are a dynamic research group at the Faculty of Computer Science and Mathematics. We focus on Computational Rhetoric techniques applied to Sentiment Analysis, Offensive language modelling and detection, Legal Tech, Argumentation Mining and other areas of NLP.

To this end, we combine this approach with argument mining, also a research area within NLP. Argumentation mining is already used in social media to identify offensive language. However, the technology has so far reached its limits. She does not recognize irony or unobtrusive insults. The team will test whether the Passau approach is capable of this in heated debates on the topics of the climate crisis and vaccination criticism.

We are also collaborating with our fellow researchers in Media Bias Group which is a research group that is focusing on uncovering media bias or unbalanced coverage.

The project on which this research group is based is funded by the German Federal Ministry of Education and Research (BMBF) along with the University of Passau.

News

28. April 2021

Dr. Jelena Mitrović has participated in 10 Minuten Soziologie zum Thema Stress, 10 Minuten Rechtswissenschaft zum Thema Digitalisierung. For the recorded session, use this link.

19. March 2021

Our Paper Automated identification of bias inducing words in news articles using linguistic and context-oriented features was accepted for publication at the Information Processing and Management Journal.

05. March 2021

Our Paper Multi-word Expressions for Abusive Language Detection in Serbian was accepted for publication at the MWE-LEX Workshop, collocated with COLING.

22. January 2021

Our paper Towards Classifying Parts of German Legal Writing Styles in German Legal Judgments was accepted for publication at the ACIT 2020 IEEE conference.

21. January 2021

Our paper Language Proficiency Scoring was accepted for publication at the LREC 2020 conference in the the first edition of the REPROLANG track.

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