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.
Data Processing and Annotation Schemes for FinCausal Shared Task
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abstract
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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Increasing the Accessibility of Causal Domain Knowledge via Causal Information Extraction Methods: A Case Study in the Semiconductor Manufacturing Industry
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.