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
Venues:
ACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
32–37
Language:
URL:
https://www.aclweb.org/anthology/W18-3104
DOI:
10.18653/v1/W18-3104
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-3104.pdf