@inproceedings{agarwal-etal-2020-langresearchlab,
title = "{L}ang{R}esearch{L}ab{\_}{NC} at {F}in{C}ausal 2020, Task 1: A Knowledge Induced Neural Net for Causality Detection",
author = "Agarwal, Raksha and
Verma, Ishaan and
Chatterjee, Niladri",
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-1.4",
pages = "33--39",
abstract = "Identifying causal relationships in a text is essential for achieving comprehensive natural language understanding. The present work proposes a combination of features derived from pre-trained BERT with linguistic features for training a supervised classifier for the task of Causality Detection. The Linguistic features help to inject knowledge about the semantic and syntactic structure of the input sentences. Experiments on the FinCausal Shared Task1 datasets indicate that the combination of Linguistic features with BERT improves overall performance for causality detection. The proposed system achieves a weighted average F1 score of 0.952 on the post-evaluation dataset.",
}
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%0 Conference Proceedings
%T LangResearchLab_NC at FinCausal 2020, Task 1: A Knowledge Induced Neural Net for Causality Detection
%A Agarwal, Raksha
%A Verma, Ishaan
%A Chatterjee, Niladri
%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 agarwal-etal-2020-langresearchlab
%X Identifying causal relationships in a text is essential for achieving comprehensive natural language understanding. The present work proposes a combination of features derived from pre-trained BERT with linguistic features for training a supervised classifier for the task of Causality Detection. The Linguistic features help to inject knowledge about the semantic and syntactic structure of the input sentences. Experiments on the FinCausal Shared Task1 datasets indicate that the combination of Linguistic features with BERT improves overall performance for causality detection. The proposed system achieves a weighted average F1 score of 0.952 on the post-evaluation dataset.
%U https://aclanthology.org/2020.fnp-1.4
%P 33-39
Markdown (Informal)
[LangResearchLab_NC at FinCausal 2020, Task 1: A Knowledge Induced Neural Net for Causality Detection](https://aclanthology.org/2020.fnp-1.4) (Agarwal et al., FNP 2020)
ACL