Causality extraction: A comprehensive survey and new perspective
Wajid Ali, Wanli Zuo, Ying Wang, Rahman Ali, Gohar Rahman, Inam Ullah
Abstract
Researchers in natural language processing are paying more attention to causality mining. Numerous applications of the growing need for efficient and accurate causality mining include question answering, future events predication, discourse comprehension, decision making, scenario generation, medical text mining, and textual entailment. Although causality has long been in the spotlight, but there are still issues that need to be addressed. This study provides a comprehensive review of casualty mining for various application domains available in the new-age literature from 1989 to 2022. We searched and rigorously examined numerous papers in the most reliable libraries for the review, and the terminologies that drive the context are described. Each paper underwent a thorough review process to extract the following meta-data: techniques, target domains, datasets, features, and limits of each approach. This meta-data will aid researchers in selecting the strategy that is most suited to their research needs. The literature is divided into three groups based on critical reviews including traditional, machine learning-based, and deep learning-based approaches. A concise taxonomy that can substantially help new scholars comprehend the field is developed. In order to make it simple for new researchers to start their research, various perspectives and suggestions are offered.