Abstract
Based on a recently developed fine-grained event extraction dataset for the economic domain, we present in a pilot study for supervised economic event extraction. We investigate how a state-of-the-art model for event extraction performs on the trigger and argument identification and classification. While F1-scores of above 50% are obtained on the task of trigger identification, we observe a large gap in performance compared to results on the benchmark ACE05 dataset. We show that single-token triggers do not provide sufficient discriminative information for a fine-grained event detection setup in a closed domain such as economics, since many classes have a large degree of lexico-semantic and contextual overlap.- Anthology ID:
- 2020.fnp-1.36
- Volume:
- Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Dr Mahmoud El-Haj, Dr Vasiliki Athanasakou, Dr Sira Ferradans, Dr Catherine Salzedo, Dr Ans Elhag, Dr Houda Bouamor, Dr Marina Litvak, Dr Paul Rayson, Dr George Giannakopoulos, Nikiforos Pittaras
- Venue:
- FNP
- SIG:
- Publisher:
- COLING
- Note:
- Pages:
- 235–245
- Language:
- URL:
- https://aclanthology.org/2020.fnp-1.36
- DOI:
- Cite (ACL):
- Gilles Jacobs and Veronique Hoste. 2020. Extracting Fine-Grained Economic Events from Business News. In Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, pages 235–245, Barcelona, Spain (Online). COLING.
- Cite (Informal):
- Extracting Fine-Grained Economic Events from Business News (Jacobs & Hoste, FNP 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.fnp-1.36.pdf