Results 1 to 10 of about 39,291 (155)

Causality extraction: A comprehensive survey and new perspective

Journal of King Saud University: Computer and Information Sciences, 2023
Researchers in natural language processing are paying more attention to causality mining. Numerous applications of the growing need for efficient and accurate causality mining include question answering, future events predication, discourse comprehension,
Wajid Ali, Wanli Zuo, Wang Ying, Rahman Ali, Gohar Rahman, Inam Ullah   +5 more
doaj   +4 more sources

Using transfer learning-based causality extraction to mine latent factors for Sjögren's syndrome from biomedical literature [PDF]

Heliyon, 2023
Understanding causality is a longstanding goal across many different domains. Different articles, such as those published in medical journals, disseminate newly discovered knowledge that is often causal.
Jack T. VanSchaik, Palak Jain, Anushri Rajapuri, Biju Cheriyan, Thankam P. Thyvalikakath, Sunandan Chakraborty   +5 more
doaj   +2 more sources

Challenges and Opportunities in Causality Analysis Using Large Language Models [PDF]

Entropy
This article examines the challenges and opportunities in extracting causal information from text with Large Language Models (LLMs). It first establishes the importance of causality extraction and then explores different views on causality, including ...
Wlodek W. Zadrozny
doaj   +2 more sources

Extracting causality from spectroscopy. [PDF]

Sci Rep
Abstract Causality represents a directed relationship where one state, designated as a cause, directly produces or partially influences another state, an effect. Identifying causality in observations of physical phenomena is a core challenge in science, as it reveals the fundamental laws governing these observations.
Fujita K, Nakayama K, Fujiki Y, Kato T, Suito H, Higuchi H, Sato T.   +6 more
europepmc   +4 more sources

Disease causality extraction based on lexical semantics and document-clause frequency from biomedical literature [PDF]

BMC Medical Informatics and Decision Making, 2017
Background Recently, research on human disease network has succeeded and has become an aid in figuring out the relationship between various diseases. In most disease networks, however, the relationship between diseases has been simply represented as an ...
Dong-gi Lee, Hyunjung Shin
doaj   +2 more sources

EEG emotion recognition based on cross-frequency granger causality feature extraction and fusion in the left and right hemispheres

Frontiers in Neuroscience, 2022
EEG emotion recognition based on Granger causality (GC) brain networks mainly focus on the EEG signal from the same-frequency bands, however, there are still some causality relationships between EEG signals in the cross-frequency bands.
Jing Zhang, Xueying Zhang, Guijun Chen, Lixia Huang, Ying Sun   +4 more
doaj   +3 more sources

A study on large-scale disease causality discovery from biomedical literature [PDF]

BMC Medical Informatics and Decision Making
Background Biomedical semantic relationship extraction could reveal important biomedical entities and the semantic relationships between them, providing a crucial foundation for the biomedical knowledge discovery, clinical decision making and other ...
Shirui Yu, Peng Dong, Junlian Li, Xiaoli Tang, Xiaoying Li   +4 more
doaj   +2 more sources

Causality extraction model based on two-stage GCN

Soft Computing, 2022
Abstract In the traditional methods, the low identification accuracy of cascade implicit causalities is caused by the lack of causal inference. To solve this problem, we propose a causality extraction model based on GCN to infer the causality of the text. It can analyze the cause-effect existing in the text and realize the deep extraction under
Shunxiang Zhang, Kuan-Ching Li, Zhu Guangli   +2 more
exaly   +2 more sources

Granger-Causality-Based Multi-Frequency Band EEG Graph Feature Extraction and Fusion for Emotion Recognition

Brain Sciences, 2022
Graph convolutional neural networks (GCN) have attracted much attention in the task of electroencephalogram (EEG) emotion recognition. However, most features of current GCNs do not take full advantage of the causal connection between the EEG signals in ...
Jing Zhang, Xueying Zhang, Guijun Chen, Qing Zhao   +3 more
doaj   +3 more sources

Causality Extraction from Medical Text Using Large Language Models (LLMs)

Information
This study explores the potential of natural language models, including large language models, to extract causal relations from medical texts, specifically from clinical practice guidelines (CPGs).
Seethalakshmi Gopalakrishnan, Luciana Garbayo, Wlodek Zadrozny   +2 more
doaj   +3 more sources