  As an essential component of human cognition, cause-effect relations appear
frequently in text, and curating cause-effect relations from text helps in
building causal networks for predictive tasks. Existing causality extraction
techniques include knowledge-based, statistical machine learning(ML)-based, and
deep learning-based approaches. Each method has its advantages and weaknesses.
For example, knowledge-based methods are understandable but require extensive
manual domain knowledge and have poor cross-domain applicability. Statistical
machine learning methods are more automated because of natural language
processing (NLP) toolkits. However, feature engineering is labor-intensive, and
toolkits may lead to error propagation. In the past few years, deep learning
techniques attract substantial attention from NLP researchers because of its'
powerful representation learning ability and the rapid increase in
computational resources. Their limitations include high computational costs and
a lack of adequate annotated training data. In this paper, we conduct a
comprehensive survey of causality extraction. We initially introduce primary
forms existing in the causality extraction: explicit intra-sentential
causality, implicit causality, and inter-sentential causality. Next, we list
benchmark datasets and modeling assessment methods for causal relation
extraction. Then, we present a structured overview of the three techniques with
their representative systems. Lastly, we highlight existing open challenges
with their potential directions.
