[HTML][HTML] Causality extraction from medical text using large language models (llms)

S Gopalakrishnan, L Garbayo, W Zadrozny - Information, 2025 - mdpi.com
This study explores the potential of natural language models, including large language
models, to extract causal relations from medical texts, specifically from clinical practice …

LERCause: Deep learning approaches for causal sentence identification from nuclear safety reports

J Kim, J Kim, A Lee, J Kim, J Diesner - PloS one, 2024 - journals.plos.org
Identifying causal sentences from nuclear incident reports is essential for advancing nuclear
safety research and applications. Nonetheless, accurately locating and labeling causal …

Knowledge graph structure as prompt: improving small language models capabilities for knowledge-based causal discovery

Y Susanti, M Färber - International Semantic Web Conference, 2024 - Springer
Causal discovery aims to estimate causal structures among variables based on
observational data. Large Language Models (LLMs) offer a fresh perspective to tackle the …

Increasing the Accessibility of Causal Domain Knowledge via Causal Information Extraction Methods: A Case Study in the Semiconductor Manufacturing Industry

H Razouk, L Benischke, D Garber, R Kern - arXiv preprint arXiv …, 2024 - arxiv.org
The extraction of causal information from textual data is crucial in the industry for identifying
and mitigating potential failures, enhancing process efficiency, prompting quality …

SciHyp: A Fine-Grained Dataset Describing Hypotheses and Their Components from Scientific Articles

R Vasu, C Sarasua, A Bernstein - International Semantic Web Conference, 2024 - Springer
Scientific discovery entails a detailed understanding and structuring of existing hypotheses—
a challenging task due to the variety and complexity of the scientific texts. Despite efforts in …

Building online public consultation knowledge graphs

W Aboucaya, S Guehis, R Angarita - … from Text, Co-located with the …, 2023 - inria.hal.science
Online consultation platforms have improved the possibilities for citizens to have an input on
public decision making. However, and especially at large scale, identification of the topics …

Causal-Evidence Graph for Causal Relation Classification

Y Susanti, K Uchino - Proceedings of the 39th ACM/SIGAPP Symposium …, 2024 - dl.acm.org
This paper aims toward an enhancement for automatic causal relation classification from text
sources. We introduce a Causal Evidence Graph (CEG), which is a graph-structured …

Prompt-based vs. Fine-tuned LLMs Toward Causal Graph Verification

Y Susanti, N Holsmoelle - arXiv preprint arXiv:2406.16899, 2024 - arxiv.org
This work aims toward an application of natural language processing (NLP) technology for
automatic verification of causal graphs using text sources. A causal graph is often derived …

Building Computational Representations of Medical Guidelines Using Large Language Models and Transfer Learning

S Gopalakrishnan - 2023 - search.proquest.com
This dissertation explores the potential of natural language models, including large
language models, to extract causal relations from medical texts, specifically from Clinical …