Abstract
This paper presents a dataset and supervised classification approach for economic event detection in English news articles. Currently, the economic domain is lacking resources and methods for data-driven supervised event detection. The detection task is conceived as a sentence-level classification task for 10 different economic event types. Two different machine learning approaches were tested: a rich feature set Support Vector Machine (SVM) set-up and a word-vector-based long short-term memory recurrent neural network (RNN-LSTM) set-up. We show satisfactory results for most event types, with the linear kernel SVM outperforming the other experimental set-ups- Anthology ID:
- W18-3101
- Volume:
- Proceedings of the First Workshop on Economics and Natural Language Processing
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1–10
- Language:
- URL:
- https://aclanthology.org/W18-3101
- DOI:
- 10.18653/v1/W18-3101
- Cite (ACL):
- Gilles Jacobs, Els Lefever, and Véronique Hoste. 2018. Economic Event Detection in Company-Specific News Text. In Proceedings of the First Workshop on Economics and Natural Language Processing, pages 1–10, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Economic Event Detection in Company-Specific News Text (Jacobs et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W18-3101.pdf