Identifying causal sentences from nuclear incident reports is essential for advancing nuclear safety research and applications. Nonetheless, accurately locating and labeling causal …
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 …
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 …
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 …
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 …
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 …
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 …
This dissertation explores the potential of natural language models, including large language models, to extract causal relations from medical texts, specifically from Clinical …