@InProceedings{theil-stajner-stuckenschmidt:2018:W18-31,
  author    = {Theil, Christoph Kilian  and  Stajner, Sanja  and  Stuckenschmidt, Heiner},
  title     = {Word Embeddings-Based Uncertainty Detection in Financial Disclosures},
  booktitle = {Proceedings of the First Workshop on Economics and Natural Language Processing},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {32--37},
  abstract  = {In this paper, we use NLP techniques to detect linguistic uncertainty in financial disclosures. Leveraging general-domain and domain-specific word embedding models, we automatically expand an existing dictionary of uncertainty triggers. We furthermore examine how an expert filtering affects the quality of such an expansion. We show that the dictionary expansions significantly improve regressions on stock return volatility. Lastly, we prove that the expansions significantly boost the automatic detection of uncertain sentences.},
  url       = {http://www.aclweb.org/anthology/W18-3104}
}