RRepoGEO

REPOGEO REPORT · LITE

styfeng/DataAug4NLP

Default branch main · commit e785952f · scanned 6/16/2026, 10:42:41 AM

GitHub: 832 stars · 77 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface styfeng/DataAug4NLP, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening to clarify its nature as a survey and paper collection

    Why:

    CURRENT
    # Data Augmentation Techniques for NLP

If you'd like to add your paper, do not email us. Instead, read the protocol for adding a new entry and send a pull request.
    COPY-PASTE FIX
    # A Comprehensive Survey and Curated Collection of Data Augmentation Papers for NLP

This repository is based on our paper, "A survey of data augmentation approaches in NLP (Findings of ACL '21)". It serves as a curated collection of papers and resources for data augmentation in Natural Language Processing.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the content of the MIT License (or another appropriate open-source license).
  • mediumtopics#3
    Add more specific topics to emphasize its role as a resource collection

    Why:

    CURRENT
    acl2021, artificial-intelligence, data-augmentation, deep-learning, machine-learning, natural-language-processing, survey, survey-paper, text-classification, transformers
    COPY-PASTE FIX
    acl2021, artificial-intelligence, data-augmentation, deep-learning, machine-learning, natural-language-processing, survey, survey-paper, text-classification, transformers, nlp-resources, paper-collection, literature-review

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface styfeng/DataAug4NLP
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Cloud Translation API
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud Translation API · recommended 1×
  2. DeepL API · recommended 1×
  3. Microsoft Translator Text API · recommended 1×
  4. WordNet · recommended 1×
  5. NLTK · recommended 1×
  • CATEGORY QUERY
    What are effective data augmentation strategies for improving NLP model performance?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Translation API
    2. DeepL API
    3. Microsoft Translator Text API
    4. WordNet
    5. NLTK
    6. Gensim
    7. spaCy
    8. NLPAug
    9. GPT-2
    10. GPT-3
    11. T5
    12. Hugging Face Transformers
    13. BERT
    14. RoBERTa
    15. XLNet

    AI recommended 15 alternatives but never named styfeng/DataAug4NLP. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a comprehensive survey of data augmentation methods for natural language processing?
    you: not recommended
    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of styfeng/DataAug4NLP?
    pass
    AI named styfeng/DataAug4NLP explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts styfeng/DataAug4NLP in production, what risks or prerequisites should they evaluate first?
    pass
    AI named styfeng/DataAug4NLP explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo styfeng/DataAug4NLP solve, and who is the primary audience?
    pass
    AI named styfeng/DataAug4NLP explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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styfeng/DataAug4NLP — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite