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Paper

An Empirical Survey of Data Augmentation for Limited Data Learning in NLP

by Independent / Community 013eb12ce5468f79d58bf859653f4929c5a2bd14

Free2AITools Nexus Index

71.2

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 89

P: Popularity 67

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

NLP has achieved great progress in the past decade through the use of neural models and large labeled datasets. The dependence on abundant data prevents NLP models from being applied to low-resource settings or novel tasks where significant time, money, or expertise is required to label massive amounts of textual data. Recently, data augmentation methods have been explored as a means of improving data efficiency in NLP. To date, there has been no systematic empirical overview of data augmenta...

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Semantic Scholar 234 Citations

Paper Information Summary
Entity Passport
Registry ID	013eb12ce5468f79d58bf859653f4929c5a2bd14
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{013eb12ce5468f79d58bf859653f4929c5a2bd14,
  author = {Unknown},
  title = {An Empirical Survey of Data Augmentation for Limited Data Learning in NLP Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/013eb12ce5468f79d58bf859653f4929c5a2bd14}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). An Empirical Survey of Data Augmentation for Limited Data Learning in NLP [Paper]. Free2AITools. https://api.semanticscholar.org/013eb12ce5468f79d58bf859653f4929c5a2bd14

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 89

Popularity (P) 67

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for An Empirical Survey of Data Augmentation for Limited Data Learning in NLP: Authority (A:89), Popularity (P:67), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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📝 Executive Summary

"NLP has achieved great progress in the past decade through the use of neural models and large labeled datasets. The dependence on abundant data prevents NLP models from being applied to low-resource settings or novel tasks where significant time, money, or expertise is required to label massive amounts of textual data. Recently, data augmentation methods have been explored as a means of improving data efficiency in NLP. To date, there has been no systematic empirical overview of data augmenta..."

❝ Cite Node

@article{Unknown2026An,
  title={An Empirical Survey of Data Augmentation for Limited Data Learning in NLP},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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📊 Research Signals

📈234CitationsSemantic Scholar

🏛️89AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

🗂️text generationField

📦Data Source: semantic_scholar

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🆔 Identity & Source

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

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
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downloads: 0
stars: null
forks: null
citations: 234

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