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A cause and effect relationship is stronger than a correlation between events, and therefore aggregated causal relations extracted from large corpora can be used in numerous applications such as question-answering and summarisation to produce superior results than traditional approaches. Techniques like logical consequence allow causal relations to be used in niche practical applications such as event prediction which is useful for diverse domains such as security and finance. Until recently, the use of causal relations was a relatively unpopular technique because the causal relation extraction techniques were problematic, and the relations returned were incomplete, error prone or simplistic. The recent adoption of language models and improved relation extractors for natural language such as Transformer-XL (Dai<jats:italic>et al<\/jats:italic>. (2019).<jats:italic>Transformer-xl: Attentive language models beyond a fixed-length context<\/jats:italic>. arXiv preprint<jats:uri xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/www.arXiv:1901.02860\">arXiv:1901.02860<\/jats:uri>) has seen a surge of research interest in the possibilities of using causal relations in practical applications. Until now, there has not been an extensive survey of the practical applications of causal relations; therefore, this survey is intended precisely to demonstrate the potential of causal relations. It is a comprehensive survey of the work on the extraction of causal relations and their applications, while also discussing the nature of causation and its representation in text.<\/jats:p>","DOI":"10.1017\/s135132492100036x","type":"journal-article","created":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T10:17:32Z","timestamp":1642673852000},"page":"361-400","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":14,"title":["A survey of the extraction and applications of causal relations"],"prefix":"10.1017","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1468-0089","authenticated-orcid":false,"given":"Brett","family":"Drury","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5779-8645","authenticated-orcid":false,"given":"Hugo","family":"Gon\u00e7alo Oliveira","sequence":"additional","affiliation":[]},{"given":"Alneu","family":"de Andrade Lopes","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2022,1,20]]},"reference":[{"key":"S135132492100036X_ref177","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1372"},{"key":"S135132492100036X_ref143","volume-title":"The Book of Why: The New Science of Cause and Effect","author":"Pearl","year":"2018"},{"key":"S135132492100036X_ref195","doi-asserted-by":"publisher","DOI":"10.1145\/3440067.3440077"},{"key":"S135132492100036X_ref87","doi-asserted-by":"publisher","DOI":"10.1145\/2505515.2505612"},{"key":"S135132492100036X_ref192","unstructured":"Yang, Z. , Dai, Z. , Yang, Y. , Carbonell, J. , Salakhutdinov, R.R. , and Le, Q.V. 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