过分自信
发表于 2025-3-23 09:54:50
Document-Level Relation Extraction with a Dependency Syntax Transformer and Supervised Contrastive Live learning with fusion knowledge captures global information among relational triples. Gaussian probability distributions are also designed to capture local information around entities. Our experiments on two document-level relation extraction datasets, CDR and GDA, have remarkable results.
surmount
发表于 2025-3-23 14:14:46
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cocoon
发表于 2025-3-23 22:05:21
Incorporating Uncertainty of Entities and Relations into Few-Shot Uncertain Knowledge Graph Embeddinion of entities and relations in the few-shot scenario. Experimental results show that our proposed method can learn better embeddings in terms of the higher accuracy in both confidence score prediction and tail entity prediction.
套索
发表于 2025-3-24 00:22:27
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narcissism
发表于 2025-3-24 02:50:31
KGSG: Knowledge Guided Syntactic Graph Model for Drug-Drug Interaction Extractionatical relation. We conducted comparative experiments and ablation studies on the DDI extraction 2013 dataset. The experimental results show that our method can effectively integrate domain knowledge and syntactic information to improve the performance of DDI extraction compared with the existing methods.
止痛药
发表于 2025-3-24 07:03:32
Conference proceedings 2022n and knowledge base construction; linked data, knowledge integration, and knowledge graph storage managements; natural language understanding and semantic computing; knowledge graph applications; and knowledge graph open resources..
inscribe
发表于 2025-3-24 14:27:46
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胶状
发表于 2025-3-24 15:10:42
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字的误用
发表于 2025-3-24 19:48:44
https://doi.org/10.1007/978-981-19-7596-7artificial intelligence; semantics; natural language processing; natural languages; information retrieva
Migratory
发表于 2025-3-25 00:23:16
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