# yuanxiaosc/Entity-Relation-Extraction

Entity and Relation Extraction Based on TensorFlow and BERT. 基于TensorFlow和BERT的管道式实体及关系抽取，2019语言与智能技术竞赛信息抽取任务解决方案。Schema based Knowledge Extraction, SKE 2019

Repository: https://github.com/yuanxiaosc/Entity-Relation-Extraction
Canonical: https://ross.abutalabs.com/products/entity-relation-extraction
Homepage: https://yuanxiaosc.github.io/2019/05/17/多关系抽取研究/
Language: Python
License Family: other
Topics: tensorflow, entity-extraction, relation-extraction, pipeline-framework, bert-model, competition-code
Last push: 2020-06-01T03:24:31+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": 2705, "days_push": 2284, "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 1230, forks 268 (observed 2026-08-28T04:04:04.136573+00:00)

## What it is
A TensorFlow and BERT based pipeline for joint entity and relation extraction, built as a solution for the 2019 Language and Intelligence Competition (SKE) information extraction task. It first classifies relation types with a multi-label classifier, then uses sequence labeling to identify entities, outputting (subject, predicate, object) triples.

## Use cases
- extract entity-relation triples from Chinese text
- build a knowledge graph from unstructured sentences
- run schema-constrained information extraction
- fine-tune BERT for relation classification and NER
- reproduce the LIC 2019 SKE competition baseline

## When to choose
- you need schema-based Chinese entity and relation extraction with BERT
- you want a reference implementation of a pipeline-style SPO triple extractor
- you are working with the SKE 2019 dataset

## When to avoid
- you need a maintained library - the repo has no license and last activity was 2020
- you use PyTorch or modern transformer tooling instead of TensorFlow 1.x
- you need joint (non-pipeline) extraction or non-Chinese languages

## Facets
- artifact type: library
- maturity: maintenance
- function: nlp, machine-learning, parser
- domain: machine-learning, artificial-intelligence
- platform: python
- tags: bert, tensorflow, entity-relation-extraction, information-extraction, chinese-nlp, sequence-labeling, knowledge-graph, competition-code, natural-language-processing

## Member repositories
- yuanxiaosc/Entity-Relation-Extraction (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:04.136573+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 2026-08-30T06:15:04.266018+00:00, confidence not recorded.
  - readme: https://github.com/yuanxiaosc/Entity-Relation-Extraction (fetched 2026-08-28T04:04:04.136573+00:00, sha 782fde690eaa)
- Data as of 2026-08-30T08:39:29.467469+00:00.