@inproceedings{r-l-m-2020-nitk,
title = "{NITK} {NLP} at {F}in{C}ausal-2020 Task 1 Using {BERT} and Linear models.",
author = "R L, Hariharan and
M, Anand Kumar",
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-1.9",
pages = "60--63",
abstract = "FinCausal-2020 is the shared task which focuses on the causality detection of factual data for financial analysis. The financial data facts don{'}t provide much explanation on the variability of these data. This paper aims to propose an efficient method to classify the data into one which is having any financial cause or not. Many models were used to classify the data, out of which SVM model gave an F-Score of 0.9435, BERT with specific fine-tuning achieved best results with F-Score of 0.9677.",
}
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%0 Conference Proceedings
%T NITK NLP at FinCausal-2020 Task 1 Using BERT and Linear models.
%A R L, Hariharan
%A M, Anand Kumar
%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 r-l-m-2020-nitk
%X FinCausal-2020 is the shared task which focuses on the causality detection of factual data for financial analysis. The financial data facts don’t provide much explanation on the variability of these data. This paper aims to propose an efficient method to classify the data into one which is having any financial cause or not. Many models were used to classify the data, out of which SVM model gave an F-Score of 0.9435, BERT with specific fine-tuning achieved best results with F-Score of 0.9677.
%U https://aclanthology.org/2020.fnp-1.9
%P 60-63
Markdown (Informal)
[NITK NLP at FinCausal-2020 Task 1 Using BERT and Linear models.](https://aclanthology.org/2020.fnp-1.9) (R L & M, FNP 2020)
ACL