# crownpku/Information-Extraction-Chinese

Chinese Named Entity Recognition with IDCNN/biLSTM+CRF, and Relation Extraction with biGRU+2ATT 中文实体识别与关系提取

Repository: https://github.com/crownpku/Information-Extraction-Chinese
Canonical: https://ross.abutalabs.com/products/information-extraction-chinese
Language: Python
License Family: other
Topics: nlp, chinese-nlp, information-extraction, relation-extraction, named-entity-recognition
Last push: 2024-02-01T21:01:38+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3311, "days_push": 944, "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 2264, forks 798 (observed 2026-08-28T04:06:32.571871+00:00)

## What it is
A collection of deep learning models for Chinese information extraction, including named entity recognition with IDCNN or biLSTM+CRF and relation extraction with biGRU+2ATT. It is organized as separate sub-projects with their own documentation, implemented in Python with TensorFlow.

## Use cases
- extract named entities from Chinese text
- recognize person, organization and location names in Chinese documents
- extract relations between entities in Chinese sentences
- train a Chinese NER model with biLSTM+CRF
- run Chinese relation extraction with attention-based biGRU
- build an information extraction pipeline for Chinese text

## When to choose
- you need Chinese-specific NER or relation extraction with classic deep learning architectures
- you want trainable TensorFlow models for Chinese entity and relation extraction
- you need reference implementations of IDCNN+CRF or biGRU+attention for Chinese NLP

## When to avoid
- you need production-grade maintained software with a license and active support
- you need multilingual or English information extraction
- you want transformer-based or LLM-era extraction models out of the box

## Facets
- artifact type: library
- maturity: maintenance
- function: nlp, machine-learning, deep-learning
- domain: machine-learning, artificial-intelligence
- platform: python
- tags: chinese-nlp, named-entity-recognition, relation-extraction, information-extraction, tensorflow, idcnn, bilstm-crf, bigru-attention, natural-language-processing

## Member repositories
- crownpku/Information-Extraction-Chinese (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:32.571871+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 2026-08-30T02:42:26.615254+00:00, confidence not recorded.
  - readme: https://github.com/crownpku/Information-Extraction-Chinese (fetched 2026-08-28T04:06:32.571871+00:00, sha a8ecbde375b2)
- Data as of 2026-08-30T08:39:29.467469+00:00.