@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",
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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<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.</abstract>
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%0 Conference Proceedings
%T GBe at FinCausal 2020, Task 2: Span-based Causality Extraction for Financial Documents
%A Becquin, Guillaume
%S Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation
%D 2020
%8 dec
%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