DSC-IITISM at FinCausal 2021: Combining POS tagging with Attention-based\n Contextual Representations for Identifying Causal Relationships in Financial\n Documents

2021/10/31 by Gunjan Haldar, Haldar, Gunjan, Aman Mittal +3
Computer Science · #Topic Modeling #Advanced Text Analysis Techniques #Sentiment Analysis and Opinion Mining

paper · pdf · doi:10.48550/arxiv.2111.00490

Abstract

Causality detection draws plenty of attention in the field of Natural\nLanguage Processing and linguistics research. It has essential applications in\ninformation retrieval, event prediction, question answering, financial\nanalysis, and market research. In this study, we explore several methods to\nidentify and extract cause-effect pairs in financial documents using\ntransformers. For this purpose, we propose an approach that combines POS\ntagging with the BIO scheme, which can be integrated with modern transformer\nmodels to address this challenge of identifying causality in a given text. Our\nbest methodology achieves an F1-Score of 0.9551, and an Exact Match Score of\n0.8777 on the blind test in the FinCausal-2021 Shared Task at the FinCausal\n2021 Workshop.\n

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