Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DSC-IITISM at FinCausal 2021: Combining POS tagging with Attention-based
  Contextual Representations for Identifying Causal Relationships in Financial
  Documents   Causality detection draws plenty of attention in the field of Natural
Language Processing and linguistics research. It has essential applications in
information retrieval, event prediction, question answering, financial
analysis, and market research. In this study, we explore several methods to
identify and extract cause-effect pairs in financial documents using
transformers. For this purpose, we propose an approach that combines POS
tagging with the BIO scheme, which can be integrated with modern transformer
models to address this challenge of identifying causality in a given text. Our
best methodology achieves an F1-Score of 0.9551, and an Exact Match Score of
0.8777 on the blind test in the FinCausal-2021 Shared Task at the FinCausal
2021 Workshop.

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The categories of this paper are: nlin
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The categories of this paper are: nucl-ex
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The categories of this paper are: math
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The categories of this paper are: gr-qc
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The categories of this paper are: mtrl-th
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The categories of this paper are: eess
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The categories of this paper are: q-fin
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The categories of this paper are: cond-mat
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The categories of this paper are: econ
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-th
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The categories of this paper are: patt-sol
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The categories of this paper are: math-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: cmp-lg