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...
Main Authors: | Tao Liu, Zunwang Ke, Yanbing Li, Wushour Silamu |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2023-01-01
|
Series: | PLoS ONE |
Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10249838/?tool=EBI |
Similar Items
-
Knowledge-enhanced prototypical network with class cluster loss for few-shot relation classification.
by: Tao Liu, et al.
Published: (2023-01-01) -
Few-Shot Image Classification Method with Feature Maps Enhancement Prototype
by: XU Huajie, LIANG Shuwei
Published: (2024-04-01) -
Few‐shot classification using Gaussianisation prototypical classifier
by: Fan Liu, et al.
Published: (2023-02-01) -
Low-Resource Named Entity Recognition via the Pre-Training Model
by: Siqi Chen, et al.
Published: (2021-05-01) -
Global Prototypical Network for Few-Shot Hyperspectral Image Classification
by: Chengye Zhang, et al.
Published: (2020-01-01)