@inproceedings{becquin-2020-gbe,
title = "{GB}e at {F}in{C}ausal 2020, Task 2: Span-based Causality Extraction for Financial Documents",
author = "Becquin, Guillaume",
editor = "El-Haj, Dr Mahmoud and
Athanasakou, Dr Vasiliki and
Ferradans, Dr Sira and
Salzedo, Dr Catherine and
Elhag, Dr Ans and
Bouamor, Dr Houda and
Litvak, Dr Marina and
Rayson, Dr Paul and
Giannakopoulos, Dr George and
Pittaras, Nikiforos",
booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "COLING",
url = "https://aclanthology.org/2020.fnp-1.5",
pages = "40--44",
abstract = "This document describes a system for causality extraction from financial documents submitted as part of the FinCausal 2020 Workshop. The main contribution of this paper is a description of the robust post-processing used to detect the number of cause and effect clauses in a document and extract them. The proposed system achieved a weighted-average F1 score of more than 95{\%} for the official blind test set during the post-evaluation phase and exact clauses match for 83{\%} of the documents.",
}
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%0 Conference Proceedings
%T GBe at FinCausal 2020, Task 2: Span-based Causality Extraction for Financial Documents
%A Becquin, Guillaume
%Y El-Haj, Dr Mahmoud
%Y Athanasakou, Dr Vasiliki
%Y Ferradans, Dr Sira
%Y Salzedo, Dr Catherine
%Y Elhag, Dr Ans
%Y Bouamor, Dr Houda
%Y Litvak, Dr Marina
%Y Rayson, Dr Paul
%Y Giannakopoulos, Dr George
%Y Pittaras, Nikiforos
%S Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation
%D 2020
%8 December
%I COLING
%C Barcelona, Spain (Online)
%F becquin-2020-gbe
%X This document describes a system for causality extraction from financial documents submitted as part of the FinCausal 2020 Workshop. The main contribution of this paper is a description of the robust post-processing used to detect the number of cause and effect clauses in a document and extract them. The proposed system achieved a weighted-average F1 score of more than 95% for the official blind test set during the post-evaluation phase and exact clauses match for 83% of the documents.
%U https://aclanthology.org/2020.fnp-1.5
%P 40-44
Markdown (Informal)
[GBe at FinCausal 2020, Task 2: Span-based Causality Extraction for Financial Documents](https://aclanthology.org/2020.fnp-1.5) (Becquin, FNP 2020)
ACL