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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/21/10064 |
| _version_ | 1797512837114888192 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T06:06:17Z |
| format | Article |
| id | doaj.art-294c402d938f4d3aa73570484483456e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T06:06:17Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-294c402d938f4d3aa73570484483456e2023-11-22T20:27:18ZengMDPI AGApplied Sciences2076-34172021-10-0111211006410.3390/app112110064Causality Mining in Natural Languages Using Machine and Deep Learning Techniques: A SurveyWajid Ali0Wanli Zuo1Rahman Ali2Xianglin Zuo3Gohar Rahman4College of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Jilin University, Changchun 130012, ChinaQuaid-e-Azam College of Commerce, University of Peshawar, Peshawar 25000, PakistanCollege of Computer Science and Technology, Jilin University, Changchun 130012, ChinaFaculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, Parit Raja, Batu Pahat 86400, MalaysiaThe 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.https://www.mdpi.com/2076-3417/11/21/10064cause-effect relationcausality surveycausality miningdeep learningcausality extractionrelation classification |
| spellingShingle | Wajid Ali Wanli Zuo Rahman Ali Xianglin Zuo Gohar Rahman Causality Mining in Natural Languages Using Machine and Deep Learning Techniques: A Survey Applied Sciences cause-effect relation causality survey causality mining deep learning causality extraction relation classification |
| title | 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_short | 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 |
| topic | cause-effect relation causality survey causality mining deep learning causality extraction relation classification |
| url | https://www.mdpi.com/2076-3417/11/21/10064 |
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