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Fraunhofer IAIS at FinCausal 2020, Tasks 1 & 2: Using Ensemble Methods and Sequence Tagging to Detect Causality in Financial Documents

: Pielka, Maren; Ladi, Anna; Chapman, Clayton; Brito, Eduardo; Ramamurthy, Rajkumar; Mayer, Paul; Wahab, Abdul; Sifa, Rafet; Bauckhage, Christian

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El-Haj, M.:
1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, FNP-FNS 2020. Proceedings : December 12, 2020, Barcelona, Spain (Online)
Barcelona, 2020
ISBN: 978-1-952148-40-8
Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation (FNP-FNS) <1, 2020, Online>
International Conference on Computational Linguistics (COLING) <28, 2020, Online>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()

The FinCausal 2020 shared task aims to detect causality on financial news and identify those parts of the causal sentences related to the underlying cause and effect. We apply ensemble-based and sequence tagging methods for identifying causality, and extracting causal subsequences. Our models yield promising results on both sub-tasks, with the prospect of further improvement given more time and computing resources. With respect to task 1, we achieved an F1 score of 0.9429 on the evaluation data, and a corresponding ranking of 12/14. For task 2, we were ranked 6/10, with an F1 score of 0.76 and an ExactMatch score of 0.1912.