The professorship is currently working on the following research topics:
Natural language processing and text mining enable the targeted processing and analysis of extensive textual data sets and are now being used in numerous research disciplines and business areas, not least due to current developments in the field of generative AI technologies. Various methods and algorithms are used that originate from neighboring subject areas and research disciplines such as artificial intelligence, data mining and psychology.
The large number of physical and digital legal documents that are created and exchanged in law firms, judicial authorities and corporate legal departments holds great potential for automatic text analysis in order to discover interesting patterns, trends and emotions that are hidden to humans and thus optimize human decision-making processes.
The aim of this research project is to investigate the use of the various methods, techniques and algorithms in this area in a legal context and to evaluate how the methods can be used beneficially and what potential, but also challenges, arise from this for the design of the various digital business processes in companies.
The aim of this research project is to investigate the role of artificial intelligence in the world of work (now and in the future) and what implications this has for our work processes and our collaboration with other individuals. Current questions, which are examined on the basis of empirical qualitative methods, focus in particular on the collaboration of hybrid teams (consisting of humans and intelligent agents).
For the legal work context, the focus is on the role of efficient knowledge management in the processing of legally relevant data. As a field of work in which highly sensitive data is processed, this includes questions relating to suitable knowledge management systems for the preservation of codified knowledge. AI-based techniques for accessing collective legal knowledge are also addressed in order to avoid knowledge silos and to make knowledge searches intuitive across all knowledge sources.
Every day, large amounts of unstructured data are generated in both private and legal organizational environments. This flood of data is accompanied by various challenges. For example, the question arises of methods for efficient and reliable automatic analysis and evaluation of sentiment- and emotion-laden concepts and statements from user-generated texts, without disregarding the context of these concepts and statements.
Sentiment analysis and the dictionaries frequently used for this purpose are one of the objects of investigation in this research project. In addition to the development and evaluation of domain-based dictionaries to capture composite concepts, the role of emotions in different types of text will be analyzed and, based on the development of a taxonomy, the role of context in the interpretation of user-generated content will be investigated.
AI-based machine translation systems as a research field of artificial intelligence (AI) enable the automated translation of natural language texts into different languages. Various AI software solutions have long since found their way into everyday private and professional life and efficiently support people in accomplishing their creative and knowledge-intensive tasks.
The evaluation of the results of such systems is usually based on technical metrics (such as the BLEU score) or on the user's perception in the context of the tasks to be performed. In contrast, the function-related role perspective, which makes it possible to capture the actual impact of AI systems on the work context, is often neglected.
The aim of the research project is to investigate this role-related perspective in more detail based on empirical-quantitative research approaches and to explore the potential of such translation services to support human task performers in coping with particularly knowledge-intensive and creative tasks. In this context, the linguistic quality of the translated results will also be examined from the perspective of human perception. In this way, recommendations for future collaboration between humans and artificial intelligence in the context of work will be derived.
