@inproceedings{ein-dor-etal-2019-financial,
title = "Financial Event Extraction Using {W}ikipedia-Based Weak Supervision",
author = "Ein-Dor, Liat and
Gera, Ariel and
Toledo-Ronen, Orith and
Halfon, Alon and
Sznajder, Benjamin and
Dankin, Lena and
Bilu, Yonatan and
Katz, Yoav and
Slonim, Noam",
editor = "Hahn, Udo and
Hoste, V{\'e}ronique and
Zhang, Zhu",
booktitle = "Proceedings of the Second Workshop on Economics and Natural Language Processing",
month = nov,
year = "2019",
address = "Hong Kong",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5102",
doi = "10.18653/v1/D19-5102",
pages = "10--15",
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.",
}
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%0 Conference Proceedings
%T Financial Event Extraction Using Wikipedia-Based Weak Supervision
%A Ein-Dor, Liat
%A Gera, Ariel
%A Toledo-Ronen, Orith
%A Halfon, Alon
%A Sznajder, Benjamin
%A Dankin, Lena
%A Bilu, Yonatan
%A Katz, Yoav
%A Slonim, Noam
%Y Hahn, Udo
%Y Hoste, Véronique
%Y Zhang, Zhu
%S Proceedings of the Second Workshop on Economics and Natural Language Processing
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong
%F ein-dor-etal-2019-financial
%X 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.
%R 10.18653/v1/D19-5102
%U https://aclanthology.org/D19-5102
%U https://doi.org/10.18653/v1/D19-5102
%P 10-15
Markdown (Informal)
[Financial Event Extraction Using Wikipedia-Based Weak Supervision](https://aclanthology.org/D19-5102) (Ein-Dor et al., 2019)
ACL
- Liat Ein-Dor, Ariel Gera, Orith Toledo-Ronen, Alon Halfon, Benjamin Sznajder, Lena Dankin, Yonatan Bilu, Yoav Katz, and Noam Slonim. 2019. Financial Event Extraction Using Wikipedia-Based Weak Supervision. In Proceedings of the Second Workshop on Economics and Natural Language Processing, pages 10–15, Hong Kong. Association for Computational Linguistics.