# thunlp/NREPapers

Must-read papers on neural relation extraction (NRE)

Repository: https://github.com/thunlp/NREPapers
Canonical: https://ross.abutalabs.com/products/nrepapers
Language: TeX
License Family: other
Topics: relation-extraction, paper-list
Last push: 2020-11-10T15:38:00+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": 2969, "days_push": 2122, "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 1021, forks 150 (observed 2026-08-28T04:03:15.735009+00:00)

## What it is
A curated list of must-read papers on neural relation extraction (NRE), maintained by THUNLP. It also catalogs relevant datasets and survey papers for the field.

## Use cases
- find must-read papers on neural relation extraction
- get started researching relation extraction in NLP
- find datasets for relation extraction like TACRED and DocRED
- survey deep learning methods for information extraction
- prepare a literature review on NRE

## When to choose
- you are a researcher or student entering the neural relation extraction field
- you need a curated reading list with links to papers and datasets

## When to avoid
- you need runnable relation extraction code - use OpenNRE instead
- you need actively updated content - the list was last updated in 2020

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: nlp, documentation
- domain: machine-learning, tutorials
- platform: python
- tags: relation-extraction, paper-list, reading-list, information-extraction, academic-papers, natural-language-processing

## Member repositories
- thunlp/NREPapers (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:15.735009+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 2026-08-30T07:08:52.541403+00:00, confidence not recorded.
  - readme: https://github.com/thunlp/NREPapers (fetched 2026-08-28T04:03:15.735009+00:00, sha 5100b74fdc79)
- Data as of 2026-08-30T08:39:29.467469+00:00.