Word Embeddings-Based Uncertainty Detection in Financial Disclosures
| Autor: | Sanja Štajner, Heiner Stuckenschmidt, Christoph Kilian Theil |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Word embedding
Computer science business.industry media_common.quotation_subject 05 social sciences 050201 accounting 02 engineering and technology computer.software_genre Financial Disclosures 0502 economics and business 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Quality (business) Artificial intelligence business computer Natural language processing Word (computer architecture) media_common |
| Zdroj: | ECONLP@ACL MADOC-University of Mannheim |
| DOI: | 10.18653/v1/w18-3104 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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