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Prompt-Contrastive Learning for Zero-Shot Relation Extraction. [PDF]
Zhong X +5 more
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STAR-GO: improving protein function prediction by learning to hierarchically integrate ontology-informed semantic embeddings. [PDF]
Akça ME +3 more
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A multi-layer annotated corpus for information extraction in Russian clinical NLP. [PDF]
Sultangaziyeva A +4 more
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eDEM-CONNECT: agitation ontology for the intelligent support of informal caregivers of people with dementia. [PDF]
Suravee S +5 more
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Research on plant knowledge graph reasoning based on dual-channel attention and topological perception. [PDF]
Wang S, Su Y, Gao H.
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The speaker’s lexical-semantic network in the tip of the tongue state
Couvreu M, Laganaro M.
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A Semantics for Means-end Relations
Synthese, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hughes, J. (author) +2 more
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1988
It is common to consider a program as a relation on the set of its possible states. The relational equations describing the behavior of programs can be object level, i.e., they refer to states and values of variables, or they can be relation level, i.e., the constants and variables in the equations range over relations. Relation level work using binary
Jules Desharnais, Nazim H. Madhavji
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It is common to consider a program as a relation on the set of its possible states. The relational equations describing the behavior of programs can be object level, i.e., they refer to states and values of variables, or they can be relation level, i.e., the constants and variables in the equations range over relations. Relation level work using binary
Jules Desharnais, Nazim H. Madhavji
openaire +2 more sources

