A survey of the extraction and applications of causal relations

Author:

Drury BrettORCID,Gonçalo Oliveira HugoORCID,de Andrade Lopes Alneu

Abstract

AbstractCausationin 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 (Daiet al. (2019).Transformer-xl: Attentive language models beyond a fixed-length context. arXiv preprintarXiv: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.

Publisher

Cambridge University Press (CUP)

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics,Software

Reference202 articles.

1. Causal Explanation Analysis on Social Media

2. Health Causal Probability Knowledge Graph

3. Mining causal topics in text data

4. Yang, Z. , Dai, Z. , Yang, Y. , Carbonell, J. , Salakhutdinov, R.R. , and Le, Q.V. (2019b). Xlnet: generalized autoregressive pretraining for language understanding. In Advances in Neural Information Processing Systems, pp. 5753–5763.

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Causal-Evidence Graph for Causal Relation Classification;Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing;2024-04-08

2. Research on the construction of event corpus with document-level causal relations for social security;Information Processing & Management;2023-11

3. The Causal Reasoning Ability of Open Large Language Model: A Comprehensive and Exemplary Functional Testing;2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security (QRS);2023-10-22

4. An APT Event Extraction Method Based on BERT-BiGRU-CRF for APT Attack Detection;Electronics;2023-08-04

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3