Abstract
Breaking news on economic events such as stock splits or mergers and acquisitions has been shown to have a substantial impact on the financial markets. As it is important to be able to automatically identify events in news items accurately and in a timely manner, we present in this paper proof-of-concept experiments for a supervised machine learning approach to economic event detection in newswire text. For this purpose, we created a corpus of Dutch financial news articles in which 10 types of company-specific economic events were annotated. We trained classifiers using various lexical, syntactic and semantic features. We obtain good results based on a basic set of shallow features, thus showing that this method is a viable approach for economic event detection in news text.- Anthology ID:
- L16-1051
- Volume:
- Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
- Month:
- May
- Year:
- 2016
- Address:
- Portorož, Slovenia
- Editors:
- Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- 330–335
- Language:
- URL:
- https://aclanthology.org/L16-1051
- DOI:
- Cite (ACL):
- Els Lefever and Véronique Hoste. 2016. A Classification-based Approach to Economic Event Detection in Dutch News Text. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 330–335, Portorož, Slovenia. European Language Resources Association (ELRA).
- Cite (Informal):
- A Classification-based Approach to Economic Event Detection in Dutch News Text (Lefever & Hoste, LREC 2016)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/L16-1051.pdf