Photo credit: Emilie Orgler

Christoph Kilian Theil


B6, 26, Room C1.05

E-mail: christoph (at)

Focus group: Artificial intelligence

LinkedIn Profile

PhD Project

Automatic content analysis in accounting and finance


  • SURV703 Computer-Based Content Analysis I (@IPSDS)
  • SURV704 Computer-Based Content Analysis II (@IPSDS)


  • Theil, C. K., Štajner, S. and 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. and 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.


  • Appendix of our 2018 ECONLP paper „Word Embeddings-Based Uncertainty Detection in Financial Disclosures“ including our dictionary expansions
  • Dataset of our 2018 ECONLP paper; sentences and labels („c“ = certain, „u“ = uncertain) are comma-separated
  • Domain-specific embedding model of our 2018 ECONLP paper
  • Dataset of our 2017 CICLing paper; sentences and labels („c“ = certain, „u“ = uncertain) are comma-separated

 If you use any of this data in your research, please cite the corresponding paper.