Prof. Dr. Felix Naumann

Project Description

Topic models automatically learn probabilistic representations for documents and their underlying semantic topics. In this project, we extend state-of-the-art topic models for new applications and compare and combine them with other document representations, such as embedding models.


  • Topic Models for Multiple Corpora
  • Combining Topic Models and Word Embeddings

Project-Related Publications

  • Risch, J., Krestel, R.: What Should I Cite? Cross-Collection Reference Recommendation of Patents and Papers. Proceedings of the International Conference on Theory and Practice of Digital Libraries (TPDL). pp. 40-46 (2017).
  • Park, J., Blume-Kohout, M., Krestel, R., Nalisnick, E., Smyth, P.: Analyzing NIH Funding Patterns over Time with Statistical Text Analysis. Scholarly Big Data: AI Perspectives, Challenges, and Ideas (SBD 2016) Workshop at AAAI 2016. AAAI (2016).