CEREX@FIRE-2020: Overview of the Shared Task on Cause-effect Relation Extraction

Abstract

Extraction of causal relations from text is an important problem in Natural Language Processing (NLP). The extracted relations play important roles in several downstream analytical and predictive tasks like identification of actionable items, question-answering and isolation of predictor variables for a predictive system. Curating causal relations from text documents can also help in automatically building causal networks which are also useful for reasoning tasks. The proposed CEREX track aims to find a suitable model for automatic detection of causal sentences and extraction of the exact cause, effect and the causal connectives from textual mentions.