RRepoGEO

REPOGEO REPORT · LITE

carrie0307/DL_EventExtractionPapers

Default branch master · commit 49c50c20 · scanned 6/1/2026, 5:28:20 PM

GitHub: 630 stars · 100 forks

AI VISIBILITY SCORE
22 /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
1 / 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 carrie0307/DL_EventExtractionPapers, 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

2 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 sentence to clarify repo type

    Why:

    CURRENT
    Based on BaptisteBlouin, I review papers about **Deep Learning based Event Extraction**, and annotate **keywords and Abbreviation of Models**. Besides, I categorized the papers as **Chinese Event Extraction, Open-domain Event Extraction, Event Data Generation, Cross-lingual Event Extraction, Few-Shot Event Extraction and Zero-Shot Event Extraction**, **Document-level EE**.
    COPY-PASTE FIX
    This repository is a curated collection of research papers on **Deep Learning based Event Extraction** since 2015. It includes annotations for **keywords and model abbreviations**, and papers are categorized by sub-domain such as **Chinese Event Extraction, Open-domain Event Extraction, Event Data Generation, Cross-lingual Event Extraction, Few-Shot Event Extraction, Zero-Shot Event Extraction, and Document-level EE**.
  • mediumabout#2
    Update the repository description to English

    Why:

    CURRENT
    2015年以来基于深度学习方法的事件抽取论文整理
    COPY-PASTE FIX
    A curated collection of research papers on Deep Learning based Event Extraction since 2015.

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 carrie0307/DL_EventExtractionPapers
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GPT-4
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. GPT-4 · recommended 2×
  2. Llama 2 · recommended 2×
  3. Claude 3 · recommended 1×
  4. Llama 3 · recommended 1×
  5. Mistral · recommended 1×
  • CATEGORY QUERY
    What are the latest deep learning methods for extracting events from unstructured text?
    you: not recommended
    AI recommended (in order):
    1. GPT-4
    2. Claude 3
    3. Llama 3
    4. Llama 2
    5. Mistral
    6. Flan-T5
    7. OneIE
    8. Text2Event
    9. T5
    10. BART
    11. BERT
    12. RoBERTa
    13. ELECTRA
    14. Graph-based Event Extraction with Argument-Specific Structures
    15. JMEE
    16. PURE

    AI recommended 16 alternatives but never named carrie0307/DL_EventExtractionPapers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find resources on few-shot and zero-shot event extraction models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library (huggingface/transformers)
    2. distilbert-base-uncased
    3. roberta-base
    4. gpt-2
    5. t5-base
    6. SetFit Library (huggingface/setfit)
    7. OpenNRE (thunlp/OpenNRE)
    8. GPT-3
    9. GPT-4
    10. Claude
    11. Llama 2

    AI recommended 11 alternatives but never named carrie0307/DL_EventExtractionPapers. This is the gap to close.

    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 carrie0307/DL_EventExtractionPapers?
    pass
    AI did not name carrie0307/DL_EventExtractionPapers — likely talking about a different project

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

  • If a team adopts carrie0307/DL_EventExtractionPapers in production, what risks or prerequisites should they evaluate first?
    pass
    AI named carrie0307/DL_EventExtractionPapers 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 carrie0307/DL_EventExtractionPapers solve, and who is the primary audience?
    pass
    AI did not name carrie0307/DL_EventExtractionPapers — likely talking about a different project

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

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carrie0307/DL_EventExtractionPapers — 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