Results 271 to 280 of about 5,797,629 (324)

Document-level biomedical relation extraction via hierarchical tree graph and relation segmentation module. [PDF]

Bioinformatics
Yuan J, Zhang F, Qiu Y, Lin H, Zhang Y.
europepmc   +1 more source

FSN: Joint Entity and Relation Extraction Based on Filter Separator Network. [PDF]

Entropy (Basel)
Dai Q, Yang W, Wei F, He L, Liao Y.
europepmc   +1 more source

Self-distillation framework for document-level relation extraction in low-resource environments. [PDF]

PeerJ Comput Sci
Wu H, Zhou G, Xia Y, Liu H, Zhang T.
europepmc   +1 more source

Beyond associations: A benchmark Causal Relation Extraction Dataset (CRED) of disease-causing genes, its comparative evaluation, interpretation and application

Bansal N, Sri Dhinesh RC, Pathak A, Narayanan M.   +3 more
europepmc   +1 more source

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Deep learning-based relation extraction and knowledge graph-based representation of construction safety requirements

Automation in Construction, 2023
10 Field compliance checking aims to check the compliance of site operations with applicable construction safety 11 regulations for detecting violations.
Xiyu Wang, N. El-Gohary
semanticscholar   +1 more source

Open Hierarchical Relation Extraction

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021
Open relation extraction (OpenRE) aims to extract novel relation types from open-domain corpora, which plays an important role in completing the relation schemes of knowledge bases (KBs). Most OpenRE methods cast different relation types in isolation without considering their hierarchical dependency.
Kai Zhang, Yuan Yao, Ruobing Xie, Xu Han, Zhiyuan Liu, Fen Lin, Leyu Lin, Maosong Sun   +7 more
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Multimodal Relation Extraction with Efficient Graph Alignment

ACM Multimedia, 2021
Relation extraction (RE) is a fundamental process in constructing knowledge graphs. However, previous methods on relation extraction suffer sharp performance decline in short and noisy social media texts due to a lack of contexts.
Changmeng Zheng, Junhao Feng, Ze Fu, Yiru Cai, Qing Li, Tao Wang   +5 more
semanticscholar   +1 more source

Ontology-Aware Biomedical Relation Extraction

2022
MotivationAutomatically extracting relationships from biomedical texts among multiple sorts of entities is an essential task in biomedical natural language processing with numerous applications, such as drug development or repurposing, precision medicine, and other biomedical tasks requiring knowledge discovery. Current Relation Extraction (RE) systems
Ahmad Aghaebrahimian, Maria Anisimova, Manuel Gil   +2 more
openaire   +1 more source