# km1994/nlp_paper_study

该仓库主要记录 NLP 算法工程师相关的顶会论文研读笔记

Repository: https://github.com/km1994/nlp_paper_study
Canonical: https://ross.abutalabs.com/products/nlp_paper_study
Language: C++
License Family: other
Topics: bert, relation-extraction, entity-recognition, attention, gcn
Last push: 2023-08-18T05:42:01+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2664, "days_push": 1111, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4033, forks 638 (observed 2026-08-28T04:08:32.785986+00:00)

## What it is
A curated collection of study notes on top-conference NLP papers, covering topics like BERT, Transformers, prompt engineering, information extraction, and knowledge graphs. It serves as a learning resource for NLP algorithm engineers, with notes also mirrored to a Feishu mind-note for mobile reading.

## Use cases
- learn nlp from top conference papers
- study notes on bert and transformer models
- understand named entity recognition and relation extraction papers
- prepare for nlp algorithm engineer interviews
- learn about prompt engineering and llms
- study knowledge graph and gcn papers

## When to choose
- you want structured Chinese-language notes on classic and modern NLP papers
- you are preparing for NLP interviews and need topic-by-topic paper summaries
- you want guided reading paths through transformer, pretraining, and information extraction literature

## When to avoid
- you need runnable production code or maintained libraries rather than notes
- you require English-language learning material
- you need actively updated content covering the latest papers

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: nlp, machine-learning, documentation
- domain: deep-learning, tutorials, large-language-models
- platform: cross-platform
- tags: study-notes, paper-reading, bert, transformer, information-extraction, knowledge-graph, chinese-language, natural-language-processing

## Member repositories
- km1994/nlp_paper_study (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:32.785986+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:23:49.617488+00:00, confidence not recorded.
  - readme: https://github.com/km1994/nlp_paper_study (fetched 2026-08-28T04:08:32.785986+00:00, sha 54dd95cb7d1d)
- Data as of 2026-08-30T08:39:29.467469+00:00.