Automatic Extraction of Causal Relations from Natural Language Texts: A Comprehensive Survey

TL;DR

This survey reviews various methods for automatically extracting causal relations from natural language texts, highlighting the shift from rule-based to machine learning approaches and discussing their respective strengths and weaknesses.

Contribution

It provides a comprehensive overview of causal relation extraction techniques, comparing rule-based and machine learning paradigms, and offers insights for future research directions.

Findings

Rule-based methods are limited to small, domain-specific datasets.

Machine learning approaches enable domain-independent causal extraction.

The survey identifies key challenges and potential future research areas.

Abstract

Automatic extraction of cause-effect relationships from natural language texts is a challenging open problem in Artificial Intelligence. Most of the early attempts at its solution used manually constructed linguistic and syntactic rules on small and domain-specific data sets. However, with the advent of big data, the availability of affordable computing power and the recent popularization of machine learning, the paradigm to tackle this problem has slowly shifted. Machines are now expected to learn generic causal extraction rules from labelled data with minimal supervision, in a domain independent-manner. In this paper, we provide a comprehensive survey of causal relation extraction techniques from both paradigms, and analyse their relative strengths and weaknesses, with recommendations for future work.