{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/gbe-at-fincausal-2020-task-2-span-based","title":"GBe at FinCausal 2020, Task 2: Span-based Causality Extraction for Financial Documents","arxiv_id":null,"date":"2020-12-01","proceeding":"FNP (COLING) 2020 12","authors":["Guillaume Becquin"],"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.","url_abs":"https://aclanthology.org/2020.fnp-1.5","url_pdf":"https://aclanthology.org/2020.fnp-1.5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"gbe-at-fincausal-2020-task-2-span-based","repo_url":"https://github.com/guillaume-be/financial-causality-extraction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"task-2","task_name":"Task 2"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}