Kilian Theil


B6, 26, Room C1.05

E-mail: kilian (at)

Focus group: Artificial intelligence

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Research Interests

  • Textual analysis in behavioral economics
    • Sentiment analysis
    • Risk prediction
  • Ethical implications of smart cities
    • Agent-based traffic simulations


  • Theil, C. K., Broscheit, S. und Stuckenschmidt, H. (2019). PRoFET: Predicting the risk of firms from event transcripts. In Kraus, S., Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019 (S. 5211-5217). , IJCAI/AAAI Press: Menlo Park, CA.
  • Theil, C. K., Štajner, S. und Stuckenschmidt, H. (2018). Word embeddings-based uncertainty detection in financial disclosures. In Hahn, U., Economics and Natural Language Processing - proceedings of the First workshop (ECONLP 2018) : July 20, 2018, Melbourne, Australia : ACL 2018 (S. 32-37). , Association for Computational Linguistics: Stroudsburg, PA.
  • Theil, C. K., Štajner, S., Stuckenschmidt, H. und Ponzetto, S. P. (2018). Automatic detection of uncertain statements in the financial domain. In Gelbukh, A., Computational Linguistics and Intelligent Text Processing : 18th International Conference, CICLing 2017, Budapest, Hungary, April 17–23, 2017, Revised Selected Papers, Part II (S. 642-654). Lecture Notes in Computer Science, Springer International Publishing: Cham.


  • Code and data of our 2020 COLING FNP paper „Predicting Modality in Financial Dialogue“
  • Code and data of our 2020 ACM-TDS paper „Explaining Financial Uncertainty Through Specialized Word Embeddings“
  • Code, data and embedding models of our 2019 IJCAI paper „PRoFET: Predicting the Risk of Firms from Event Transcripts“
  • Appendix and embedding model of our 2018 ACL ECONLP paper „Word Embeddings-Based Uncertainty Detection in Financial Disclosures“
  • Dataset of our 2017 CICLing paper; sentences and labels („c“ = certain, „u“ = uncertain) are comma-separated