Abstract
In this paper describes the approach which we have built for causality extraction from the financial documents that we have submitted for FinCausal 2022 task 2. We proving a solution with intelligent pre-processing and post-processing to detect the number of cause and effect in a financial document and extract them. Our given approach achieved 90% as F1 score(weighted-average) for the official blind evaluation dataset.- Anthology ID:
- 2022.fnp-1.22
- Volume:
- Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Mahmoud El-Haj, Paul Rayson, Nadhem Zmandar
- Venue:
- FNP
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 128–130
- Language:
- URL:
- https://aclanthology.org/2022.fnp-1.22
- DOI:
- Cite (ACL):
- Joydeb Mondal, Nagaraj Bhat, Pramir Sarkar, and Shahid Reza. 2022. ExpertNeurons at FinCausal 2022 Task 2: Causality Extraction for Financial Documents. In Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022, pages 128–130, Marseille, France. European Language Resources Association.
- Cite (Informal):
- ExpertNeurons at FinCausal 2022 Task 2: Causality Extraction for Financial Documents (Mondal et al., FNP 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.fnp-1.22.pdf