@inproceedings{pielka-etal-2020-fraunhofer,
title = "Fraunhofer {IAIS} at {F}in{C}ausal 2020, Tasks 1 {\&} 2: Using Ensemble Methods and Sequence Tagging to Detect Causality in Financial Documents",
author = "Pielka, Maren and
Ramamurthy, Rajkumar and
Ladi, Anna and
Brito, Eduardo and
Chapman, Clayton and
Mayer, Paul and
Sifa, Rafet",
editor = "El-Haj, Dr Mahmoud and
Athanasakou, Dr Vasiliki and
Ferradans, Dr Sira and
Salzedo, Dr Catherine and
Elhag, Dr Ans and
Bouamor, Dr Houda and
Litvak, Dr Marina and
Rayson, Dr Paul and
Giannakopoulos, Dr George and
Pittaras, Nikiforos",
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-1.10",
pages = "64--68",
abstract = "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.",
}
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%0 Conference Proceedings
%T Fraunhofer IAIS at FinCausal 2020, Tasks 1 & 2: Using Ensemble Methods and Sequence Tagging to Detect Causality in Financial Documents
%A Pielka, Maren
%A Ramamurthy, Rajkumar
%A Ladi, Anna
%A Brito, Eduardo
%A Chapman, Clayton
%A Mayer, Paul
%A Sifa, Rafet
%Y El-Haj, Dr Mahmoud
%Y Athanasakou, Dr Vasiliki
%Y Ferradans, Dr Sira
%Y Salzedo, Dr Catherine
%Y Elhag, Dr Ans
%Y Bouamor, Dr Houda
%Y Litvak, Dr Marina
%Y Rayson, Dr Paul
%Y Giannakopoulos, Dr George
%Y Pittaras, Nikiforos
%S Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation
%D 2020
%8 December
%I COLING
%C Barcelona, Spain (Online)
%F pielka-etal-2020-fraunhofer
%X 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.
%U https://aclanthology.org/2020.fnp-1.10
%P 64-68
Markdown (Informal)
[Fraunhofer IAIS at FinCausal 2020, Tasks 1 & 2: Using Ensemble Methods and Sequence Tagging to Detect Causality in Financial Documents](https://aclanthology.org/2020.fnp-1.10) (Pielka et al., FNP 2020)
ACL
- Maren Pielka, Rajkumar Ramamurthy, Anna Ladi, Eduardo Brito, Clayton Chapman, Paul Mayer, and Rafet Sifa. 2020. Fraunhofer IAIS at FinCausal 2020, Tasks 1 & 2: Using Ensemble Methods and Sequence Tagging to Detect Causality in Financial Documents. In Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, pages 64–68, Barcelona, Spain (Online). COLING.