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Data Processing and Annotation Schemes for FinCausal Shared Task

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arxiv 2012.02498 v1 pith:6ICJI6EL submitted 2020-12-04 cs.CL

classification cs.CL
keywords taskannotationdatafinancialfincausalprocessingschemesshared
verification ladder T0 review T1 audit T2 compute T3 formal
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This document explains the annotation schemes used to label the data for the FinCausal Shared Task (Mariko et al., 2020). This task is associated to the Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation (FNP-FNS 2020), to be held at The 28th International Conference on Computational Linguistics (COLING'2020), on December 12, 2020.

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  1. Increasing the Accessibility of Causal Domain Knowledge via Causal Information Extraction Methods: A Case Study in the Semiconductor Manufacturing Industry

    cs.CL 2024-11 conditional novelty 4.0 of 10

    A multi-stage sequence tagging method adapted from prior work extracts causal relations from semiconductor FMEA documents at 93% F1 and from presentation slides at 73% F1, on a private dataset.

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