ISIKUN at the FinCausal 2020: Linguistically informed Machine-learning Approach for Causality Identification in Financial Documents

Gökberk Özenir, İlknur Karadeniz


Abstract
This paper presents our participation to the FinCausal-2020 Shared Task whose ultimate aim is to extract cause-effect relations from a given financial text. Our participation includes two systems for the two sub-tasks of the FinCausal-2020 Shared Task. The first sub-task (Task-1) consists of the binary classification of the given sentences as causal meaningful (1) or causal meaningless (0). Our approach for the Task-1 includes applying linear support vector machines after transforming the input sentences into vector representations using term frequency-inverse document frequency scheme with 3-grams. The second sub-task (Task-2) consists of the identification of the cause-effect relations in the sentences, which are detected as causal meaningful. Our approach for the Task-2 is a CRF-based model which uses linguistically informed features. For the Task-1, the obtained results show that there is a small difference between the proposed approach based on linear support vector machines (F-score 94%) , which requires less time compared to the BERT-based baseline (F-score 95%). For the Task-2, although a minor modifications such as the learning algorithm type and the feature representations are made in the conditional random fields based baseline (F-score 52%), we have obtained better results (F-score 60%). The source codes for the both tasks are available online (https://github.com/ozenirgokberk/FinCausal2020.git/).
Anthology ID:
2020.fnp-1.14
Volume:
Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
FNP
SIG:
Publisher:
COLING
Note:
Pages:
85–89
Language:
URL:
https://aclanthology.org/2020.fnp-1.14
DOI:
Bibkey:
Cite (ACL):
Gökberk Özenir and İlknur Karadeniz. 2020. ISIKUN at the FinCausal 2020: Linguistically informed Machine-learning Approach for Causality Identification in Financial Documents. In Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, pages 85–89, Barcelona, Spain (Online). COLING.
Cite (Informal):
ISIKUN at the FinCausal 2020: Linguistically informed Machine-learning Approach for Causality Identification in Financial Documents (Özenir & Karadeniz, FNP 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2020.fnp-1.14.pdf
Code
 ozenirgokberk/fincausal2020