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WC-SBERT: Zero-Shot Text Classification via SBERT with Self-Training for Wikipedia Categories paper by Independent / Community
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Paper

WC-SBERT: Zero-Shot Text Classification via SBERT with Self-Training for Wikipedia Categories

by Independent / Community 029fddaf2cfb690ad4392cba62a7bfba7c52a717
Free2AITools Nexus Index
62.3
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 68
P: Popularity 43
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

Our research focuses on solving the zero-shot text classification problem in NLP, with a particular emphasis on innovative self-training strategies. To achieve this objective, we propose a novel self-training strategy that uses labels rather than text for training, significantly reducing the model's training time. Specifically, we use categories from Wikipedia as our training set and leverage the SBERT pre-trained model to establish positive correlations between pairs of categories within the...

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Semantic Scholar 3 Citations
Paper Information Summary
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Registry ID 029fddaf2cfb690ad4392cba62a7bfba7c52a717
License ArXiv
Provider semantic_scholar
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{029fddaf2cfb690ad4392cba62a7bfba7c52a717,
  author = {Unknown},
  title = {WC-SBERT: Zero-Shot Text Classification via SBERT with Self-Training for Wikipedia Categories Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/029fddaf2cfb690ad4392cba62a7bfba7c52a717}},
  note = {Accessed via Free2AITools.}
}
APA Style
Unknown. (2026). WC-SBERT: Zero-Shot Text Classification via SBERT with Self-Training for Wikipedia Categories [Paper]. Free2AITools. https://api.semanticscholar.org/029fddaf2cfb690ad4392cba62a7bfba7c52a717

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βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 68
Popularity (P) 43
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for WC-SBERT: Zero-Shot Text Classification via SBERT with Self-Training for Wikipedia Categories: Authority (A:68), Popularity (P:43), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

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πŸ“ Executive Summary

"Our research focuses on solving the zero-shot text classification problem in NLP, with a particular emphasis on innovative self-training strategies. To achieve this objective, we propose a novel self-training strategy that uses labels rather than text for training, significantly reducing the model's training time. Specifically, we use categories from Wikipedia as our training set and leverage the SBERT pre-trained model to establish positive correlations between pairs of categories within the..."

❝ Cite Node 

@article{Unknown2026WC-SBERT:,
  title={WC-SBERT: Zero-Shot Text Classification via SBERT with Self-Training for Wikipedia Categories},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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πŸ“Š Research Signals

πŸ“ˆ3CitationsSemantic Scholar
πŸ›οΈ68AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
πŸ—‚οΈknowledge retrievalField

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source
semantic_scholar
author
Unknown
license
ArXiv
tags
paper, research, academic

βš™οΈ Technical Specs

architecture
null
params billions
null
context length
null
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citations
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