Economic event detection in company-specific news text
- Publication type
- P1
- Publication status
- Published
- Authors
- Jacobs, G.M., Lefever, E., & Hoste, V.
- Editor
- Udo Hahn, Veronique Hoste and Ming-Feng Tsai
- Series
- ECONOMICS AND NATURAL LANGUAGE PROCESSING (ECONLP 2018)
- Pagination
- 1-10
- Publisher
- Association for Computational Linguistics (ACL) (Melbourne, Australia)
- Conference
- 1st Workshop on Economics and Natural Language Processing (ECONLP) at Meeting of the Association-for-Computational-Linguistics (ACL) (Melbourne, Australia)
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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.