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A survey of the extraction and applications of causal relations

Published online by Cambridge University Press:  20 January 2022

Brett Drury Open the ORCID record for Brett Drury [Opens in a new window] ,
Hugo Gonçalo Oliveira Open the ORCID record for Hugo Gonçalo Oliveira [Opens in a new window]  and
Alneu de Andrade Lopes
Brett Drury*
Affiliation:
LIAAD INESC Tec, R. Dr. Roberto Frias, Porto, Portugal
Hugo Gonçalo Oliveira
Affiliation:
CISUC, Department of Informatics Engineering, Universidade de Coimbra, Coimbra, Portugal
Alneu de Andrade Lopes
Affiliation:
ICMC, USP Av. Trab. São Carlense, São Paulo, Brazil
*
*Corresponding author. E-mail: brett.drury@gmail.com
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Abstract

Causationin written natural language can express a strong relationship between events and facts. Causation in the written form can be referred to as a causal relation where a cause event entails the occurrence of an effect event. 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 et al. (2019). Transformer-xl: Attentive language models beyond a fixed-length context. arXiv preprint arXiv:1901.02860) 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.

Keywords

Causal relationsSurveySentimentEvent predictionInformation retrievalCause identification
Type
Survey Paper
Information
Natural Language Engineering , Volume 28 , Issue 3 , May 2022 , pp. 361 - 400
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© The Author(s), 2022. Published by Cambridge University Press

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