Drury, B. M. and Gonçalo Oliveira, H and de Andrade Lopes, A (2021) A Survey of the Extraction and Applications of Causal Relations. Natural Language Engineering. ISSN 1351-3249 (Accepted for Publication)
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Abstract
Causation in 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 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.
| Item Type: | Article |
|---|---|
| Keywords: | Causal Relations Survey Sentiment Event Prediction Information Retrieval Cause Identification |
| Faculty / Department: | Faculty of Human and Digital Sciences > Mathematics and Computer Science |
| Depositing User: | Brett Drury |
| Date Deposited: | 05 Nov 2021 13:22 |
| Last Modified: | 01 Jun 2022 00:15 |
| URI: | https://hira.hope.ac.uk/id/eprint/3409 |
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