Financial Event Extraction Using Wikipedia-Based Weak Supervision

Liat Ein-Dor, Ariel Gera, Orith Toledo-Ronen, Alon Halfon, Benjamin Sznajder, Lena Dankin, Yonatan Bilu, Yoav Katz, Noam Slonim


Abstract
Extraction of financial and economic events from text has previously been done mostly using rule-based methods, with more recent works employing machine learning techniques. This work is in line with this latter approach, leveraging relevant Wikipedia sections to extract weak labels for sentences describing economic events. Whereas previous weakly supervised approaches required a knowledge-base of such events, or corresponding financial figures, our approach requires no such additional data, and can be employed to extract economic events related to companies which are not even mentioned in the training data.
Anthology ID:
D19-5102
Volume:
Proceedings of the Second Workshop on Economics and Natural Language Processing
Month:
November
Year:
2019
Address:
Hong Kong
Venues:
EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10–15
Language:
URL:
https://www.aclweb.org/anthology/D19-5102
DOI:
10.18653/v1/D19-5102
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PDF:
http://aclanthology.lst.uni-saarland.de/D19-5102.pdf