LIPI at FinCausal 2022: Mining Causes and Effects from Financial Texts

EasyChair Preprint 8115

Abstract

While reading financial documents, investors need to know the causes and their effects. This empowers them to make data-driven decisions. Thus, there is a need to develop an automated system for extracting causes and their effects from financial texts using Natural Language Processing. In this paper, we present the approach our team LIPI followed while participating in the FinCausal 2022 shared task. This approach is based on the winning solution of the first edition of FinCausal held in the year 2020.

Keyphrases: Causality extraction, Financial texts, Natural Language Processing

@booklet{EasyChair:8115,
  author    = {Sohom Ghosh and Sudip Kumar Naskar},
  title     = {LIPI at FinCausal 2022: Mining Causes and Effects from Financial Texts},
  howpublished = {EasyChair Preprint 8115},
  year      = {EasyChair, 2022}}