Natural Language Processing

In the field of Knowledge-Enriched NLP, we work on current topics of Natural Language Processing. Specifically, we are adapting and improving large language models (LLMs) such as BERT and its derivatives. Our particular focus lies in incorporating explicit knowledge, such as knowledge graphs.

Our application areas range from analyzing historical literature (where current language models struggle due to the length of the texts) to product reviews, and even to unconventional media forms for NLP, such as comments on http://twitch.tv. These media forms present their own challenges due to their unique language style. In addition to analyzing pure text, we also investigate the adaptability of NLP methods for processing mathematical equations.

In projects like Kallimachos or CLiGS we collaborate with literary scholars and work on literary and NLP research questions. In MOTIV, we work with psychologists to analyse the interaction between users and smart devices.

• Kallimachos - Building a complete text analysis pipeline, starting with OCR from paper and going up to high-level text mining.

• CLiGS - CLiGS combines large text collections with innovative analysis methods and hermeneutic sensibility for context.