Christoph Kilian Theil


Word Embeddings-Based Uncertainty Detection in Financial Disclosures
Christoph Kilian Theil | Sanja Štajner | Heiner Stuckenschmidt
Proceedings of the First Workshop on Economics and Natural Language Processing

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.