Cargando…
Knowledge-enhanced prototypical network with class cluster loss for few-shot relation classification
Few-shot Relation Classification identifies the relation between target entity pairs in unstructured natural language texts by training on a small number of labeled samples. Recent prototype network-based studies have focused on enhancing the prototype representation capability of models by incorpor...
Autores principales: | Liu, Tao, Ke, Zunwang, Li, Yanbing, Silamu, Wushour |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10249838/ https://www.ncbi.nlm.nih.gov/pubmed/37289767 http://dx.doi.org/10.1371/journal.pone.0286915 |
Ejemplares similares
-
Scene Uyghur Recognition Based on Visual Prediction Enhancement
por: Liu, Yaqi, et al.
Publicado: (2023) -
Few Shot Class Incremental Learning via Efficient Prototype Replay and Calibration
por: Zhang, Wei, et al.
Publicado: (2023) -
Display-Semantic Transformer for Scene Text Recognition
por: Yang, Xinqi, et al.
Publicado: (2023) -
CLIP-Driven Prototype Network for Few-Shot Semantic Segmentation
por: Guo, Shi-Cheng, et al.
Publicado: (2023) -
Weakly Correlated Knowledge Integration for Few-shot Image Classification
por: Yang, Chun, et al.
Publicado: (2022)