FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3165168781> ?p ?o ?g. }

• W3165168781 endingPage "1" @default.
• W3165168781 startingPage "1" @default.
• W3165168781 abstract "Label noise and long-tailed distributions are two major challenges in distantly supervised relation extraction. Recent studies have shown great progress on denoising, but paid little attention to the problem of long-tailed relations. In this paper, we introduce a constraint graph to model the dependencies between relation labels. On top of that, we further propose a novel constraint graph-based relation extraction framework(CGRE) to handle the two challenges simultaneously. CGRE employs graph convolution networks to propagate information from data-rich relation nodes to data-poor relation nodes, and thus boosts the representation learning of long-tailed relations. To further improve the noise immunity, a constraint-aware attention module is designed in CGRE to integrate the constraint information. Extensive experimental results indicate that CGRE achieves significant improvements over the previous methods for both denoising and long-tailed relation extraction." @default.
• W3165168781 created "2021-06-07" @default.
• W3165168781 creator A5004349988 @default.
• W3165168781 creator A5017777776 @default.
• W3165168781 creator A5032817846 @default.
• W3165168781 creator A5061146441 @default.
• W3165168781 creator A5079800756 @default.
• W3165168781 date "2022-01-01" @default.
• W3165168781 modified "2023-10-16" @default.
• W3165168781 title "Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs" @default.
• W3165168781 cites W1515300998 @default.
• W3165168781 cites W1604644367 @default.
• W3165168781 cites W2099779943 @default.
• W3165168781 cites W2107598941 @default.
• W3165168781 cites W2171179180 @default.
• W3165168781 cites W2251135946 @default.
• W3165168781 cites W2515462165 @default.
• W3165168781 cites W2547349506 @default.
• W3165168781 cites W2559328905 @default.
• W3165168781 cites W2600702321 @default.
• W3165168781 cites W2604610161 @default.
• W3165168781 cites W2757848402 @default.
• W3165168781 cites W2759211898 @default.
• W3165168781 cites W2760600531 @default.
• W3165168781 cites W2766317792 @default.
• W3165168781 cites W2890021306 @default.
• W3165168781 cites W2891417293 @default.
• W3165168781 cites W2892316911 @default.
• W3165168781 cites W2919278763 @default.
• W3165168781 cites W2931010691 @default.
• W3165168781 cites W2950540372 @default.
• W3165168781 cites W2951274974 @default.
• W3165168781 cites W2952402849 @default.
• W3165168781 cites W2960538829 @default.
• W3165168781 cites W2963443341 @default.
• W3165168781 cites W2963777632 @default.
• W3165168781 cites W2964317478 @default.
• W3165168781 cites W2965322655 @default.
• W3165168781 cites W2970288995 @default.
• W3165168781 cites W2971136144 @default.
• W3165168781 cites W2998010813 @default.
• W3165168781 cites W2998209348 @default.
• W3165168781 cites W2998741377 @default.
• W3165168781 cites W3003507729 @default.
• W3165168781 cites W3017074538 @default.
• W3165168781 cites W3091517605 @default.
• W3165168781 cites W3099636989 @default.
• W3165168781 cites W3104390324 @default.
• W3165168781 cites W3114142812 @default.
• W3165168781 cites W3115921524 @default.
• W3165168781 cites W3167044114 @default.
• W3165168781 cites W3167136668 @default.
• W3165168781 cites W3173600061 @default.
• W3165168781 cites W3180352486 @default.
• W3165168781 cites W3197369495 @default.
• W3165168781 cites W3197984001 @default.
• W3165168781 cites W3100536164 @default.
• W3165168781 doi "https://doi.org/10.1109/tkde.2022.3177226" @default.
• W3165168781 hasPublicationYear "2022" @default.
• W3165168781 type Work @default.
• W3165168781 sameAs 3165168781 @default.
• W3165168781 citedByCount "3" @default.
• W3165168781 countsByYear W31651687812022 @default.
• W3165168781 countsByYear W31651687812023 @default.
• W3165168781 crossrefType "journal-article" @default.
• W3165168781 hasAuthorship W3165168781A5004349988 @default.
• W3165168781 hasAuthorship W3165168781A5017777776 @default.
• W3165168781 hasAuthorship W3165168781A5032817846 @default.
• W3165168781 hasAuthorship W3165168781A5061146441 @default.
• W3165168781 hasAuthorship W3165168781A5079800756 @default.
• W3165168781 hasBestOaLocation W31651687812 @default.
• W3165168781 hasConcept C11413529 @default.
• W3165168781 hasConcept C115961682 @default.
• W3165168781 hasConcept C119857082 @default.
• W3165168781 hasConcept C124101348 @default.
• W3165168781 hasConcept C132525143 @default.
• W3165168781 hasConcept C153180895 @default.
• W3165168781 hasConcept C153604712 @default.
• W3165168781 hasConcept C154945302 @default.
• W3165168781 hasConcept C163294075 @default.
• W3165168781 hasConcept C17744445 @default.
• W3165168781 hasConcept C199539241 @default.
• W3165168781 hasConcept C2524010 @default.
• W3165168781 hasConcept C25343380 @default.
• W3165168781 hasConcept C2776036281 @default.
• W3165168781 hasConcept C2776359362 @default.
• W3165168781 hasConcept C33923547 @default.
• W3165168781 hasConcept C41008148 @default.
• W3165168781 hasConcept C45347329 @default.
• W3165168781 hasConcept C50644808 @default.
• W3165168781 hasConcept C80444323 @default.
• W3165168781 hasConcept C94625758 @default.
• W3165168781 hasConcept C99498987 @default.
• W3165168781 hasConceptScore W3165168781C11413529 @default.
• W3165168781 hasConceptScore W3165168781C115961682 @default.
• W3165168781 hasConceptScore W3165168781C119857082 @default.
• W3165168781 hasConceptScore W3165168781C124101348 @default.
• W3165168781 hasConceptScore W3165168781C132525143 @default.

• next