Financial Event Extraction Using Wikipedia-Based Weak Supervision
Computation and Language
2022-11-29 v2
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.
Keywords
Cite
@article{arxiv.1911.10783,
title = {Financial Event Extraction Using Wikipedia-Based Weak Supervision},
author = {Liat Ein-Dor and Ariel Gera and Orith Toledo-Ronen and Alon Halfon and Benjamin Sznajder and Lena Dankin and Yonatan Bilu and Yoav Katz and Noam Slonim},
journal= {arXiv preprint arXiv:1911.10783},
year = {2022}
}