To Know the Causes of Things: Text Mining for Causal Relations

Abstract

Causality expresses the relation between two arguments, one of which represents the cause and the other the effect (or consequence). Causal text mining refers to the extraction and usage of causal information from text. Given an input sequence, we are interested to know if and where causal information occurs. My research is focused on the end-to-end challenges of causal text mining. This involves extracting, representing, and applying causal knowledge from unstructured text. The corresponding research questions are: (1) How to extract causal information from unstructured text effectively? (2) How to represent extracted causal relationships in a graph that is interpretable and useful for some application? (3) How can we capitalize on extracted causal knowledge for downstream tasks? What tasks or fields will benefit from such knowledge? In this paper, I outline past and on-going works, and highlight future research challenges.