Self-Supervised and Few-Shot Contrastive Learning Frameworks for Text Clustering

Self-Supervised and Few-Shot Contrastive Learning Frameworks for Text Clustering

Contrastive learning is a promising approach to unsupervised learning, as it inherits the advantages of well-studied deep models without a dedicated and complex model design. In this paper, based on bidirectional encoder representations from transformers (BERT) and long-short term memory (LSTM) neur...

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Bibliographic Details
Main Authors:	Haoxiang Shi, Tetsuya Sakai
Format:	Article
Language:	English
Published:	IEEE 2023-01-01
Series:	IEEE Access
Subjects:	Clustering methods data augmentation self-supervised learning semisupervised learning
Online Access:	https://ieeexplore.ieee.org/document/10210342/

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