Economic Event Detection in Company-Specific News Text

Gilles Jacobs, Els Lefever, Véronique Hoste


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
Editors:
Udo Hahn, Véronique Hoste, Ming-Feng Tsai
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/W18-3101.pdf