Word Embeddings-Based Uncertainty Detection in Financial Disclosures
Christoph Kilian Theil, Sanja Štajner, Heiner Stuckenschmidt
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.- Anthology ID:
- W18-3104
- Volume:
- Proceedings of the First Workshop on Economics and Natural Language Processing
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Udo Hahn, Véronique Hoste, Ming-Feng Tsai
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 32–37
- Language:
- URL:
- https://aclanthology.org/W18-3104
- DOI:
- 10.18653/v1/W18-3104
- Cite (ACL):
- Christoph Kilian Theil, Sanja Štajner, and Heiner Stuckenschmidt. 2018. Word Embeddings-Based Uncertainty Detection in Financial Disclosures. In Proceedings of the First Workshop on Economics and Natural Language Processing, pages 32–37, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Word Embeddings-Based Uncertainty Detection in Financial Disclosures (Theil et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/W18-3104.pdf