Causality Mining in Natural Languages Using Machine and Deep Learning Techniques: A Survey

The era of big textual corpora and machine learning technologies have paved the way for researchers in numerous data mining fields. Among them, causality mining (CM) from textual data has become a significant area of concern and has more attention from researchers. Causality (cause-effect relations)...

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Autores principales:	Wajid Ali, Wanli Zuo, Rahman Ali, Xianglin Zuo, Gohar Rahman
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	cause-effect relation causality survey causality mining deep learning causality extraction relation classification Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/294c402d938f4d3aa73570484483456e
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spelling	oai:doaj.org-article:294c402d938f4d3aa73570484483456e2021-11-11T15:08:23ZCausality Mining in Natural Languages Using Machine and Deep Learning Techniques: A Survey10.3390/app1121100642076-3417https://doaj.org/article/294c402d938f4d3aa73570484483456e2021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10064https://doaj.org/toc/2076-3417The era of big textual corpora and machine learning technologies have paved the way for researchers in numerous data mining fields. Among them, causality mining (CM) from textual data has become a significant area of concern and has more attention from researchers. Causality (cause-effect relations) serves as an essential category of relationships, which plays a significant role in question answering, future events predication, discourse comprehension, decision making, future scenario generation, medical text mining, behavior prediction, and textual prediction entailment. While, decades of development techniques for CM are still prone to performance enhancement, especially for ambiguous and implicitly expressed causalities. The ineffectiveness of the early attempts is mainly due to small, ambiguous, heterogeneous, and domain-specific datasets constructed by manually linguistic and syntactic rules. Many researchers have deployed shallow machine learning (ML) and deep learning (DL) techniques to deal with such datasets, and they achieved satisfactory performance. In this survey, an effort has been made to address a comprehensive review of some state-of-the-art shallow ML and DL approaches in CM. We present a detailed taxonomy of CM and discuss popular ML and DL approaches with their comparative weaknesses and strengths, applications, popular datasets, and frameworks. Lastly, the future research challenges are discussed with illustrations of how to transform them into productive future research directions.Wajid AliWanli ZuoRahman AliXianglin ZuoGohar RahmanMDPI AGarticlecause-effect relationcausality surveycausality miningdeep learningcausality extractionrelation classificationTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10064, p 10064 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cause-effect relation causality survey causality mining deep learning causality extraction relation classification Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	cause-effect relation causality survey causality mining deep learning causality extraction relation classification Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Wajid Ali Wanli Zuo Rahman Ali Xianglin Zuo Gohar Rahman Causality Mining in Natural Languages Using Machine and Deep Learning Techniques: A Survey
description	The era of big textual corpora and machine learning technologies have paved the way for researchers in numerous data mining fields. Among them, causality mining (CM) from textual data has become a significant area of concern and has more attention from researchers. Causality (cause-effect relations) serves as an essential category of relationships, which plays a significant role in question answering, future events predication, discourse comprehension, decision making, future scenario generation, medical text mining, behavior prediction, and textual prediction entailment. While, decades of development techniques for CM are still prone to performance enhancement, especially for ambiguous and implicitly expressed causalities. The ineffectiveness of the early attempts is mainly due to small, ambiguous, heterogeneous, and domain-specific datasets constructed by manually linguistic and syntactic rules. Many researchers have deployed shallow machine learning (ML) and deep learning (DL) techniques to deal with such datasets, and they achieved satisfactory performance. In this survey, an effort has been made to address a comprehensive review of some state-of-the-art shallow ML and DL approaches in CM. We present a detailed taxonomy of CM and discuss popular ML and DL approaches with their comparative weaknesses and strengths, applications, popular datasets, and frameworks. Lastly, the future research challenges are discussed with illustrations of how to transform them into productive future research directions.
format	article
author	Wajid Ali Wanli Zuo Rahman Ali Xianglin Zuo Gohar Rahman
author_facet	Wajid Ali Wanli Zuo Rahman Ali Xianglin Zuo Gohar Rahman
author_sort	Wajid Ali
title	Causality Mining in Natural Languages Using Machine and Deep Learning Techniques: A Survey
title_short	Causality Mining in Natural Languages Using Machine and Deep Learning Techniques: A Survey
title_full	Causality Mining in Natural Languages Using Machine and Deep Learning Techniques: A Survey
title_fullStr	Causality Mining in Natural Languages Using Machine and Deep Learning Techniques: A Survey
title_full_unstemmed	Causality Mining in Natural Languages Using Machine and Deep Learning Techniques: A Survey
title_sort	causality mining in natural languages using machine and deep learning techniques: a survey
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/294c402d938f4d3aa73570484483456e
work_keys_str_mv	AT wajidali causalitymininginnaturallanguagesusingmachineanddeeplearningtechniquesasurvey AT wanlizuo causalitymininginnaturallanguagesusingmachineanddeeplearningtechniquesasurvey AT rahmanali causalitymininginnaturallanguagesusingmachineanddeeplearningtechniquesasurvey AT xianglinzuo causalitymininginnaturallanguagesusingmachineanddeeplearningtechniquesasurvey AT goharrahman causalitymininginnaturallanguagesusingmachineanddeeplearningtechniquesasurvey
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Causality Mining in Natural Languages Using Machine and Deep Learning Techniques: A Survey

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