# MenglinLu/Chinese-clinical-NER

<https://repo.masters-masters.com/en/repos/MenglinLu/Chinese-clinical-NER>

[Home](https://repo.masters-masters.com/en) › [MenglinLu](https://repo.masters-masters.com/en/owners/MenglinLu) › [Chinese-clinical-NER](https://repo.masters-masters.com/en/repos/MenglinLu/Chinese-clinical-NER)

MenglinLu/Chinese-clinical-NER has 351 stars on GitHub. Its RepoMasters card: Alpha tier. CCKS2019中文命名实体识别任务。从医疗文本中识别疾病和诊断、解剖部位、影像检查、实验室检验、手术和药物6种命名实体。现已实现基于ji…

| Field | Value |
| --- | --- |
| Owner | [MenglinLu](https://repo.masters-masters.com/en/owners/MenglinLu) |
| Stars | 351 |
| Forks | 77 |
| Language | [Python](https://repo.masters-masters.com/en/languages/python) |
| License | None |
| Created | 2019 |
| Tier | [Alpha (250 to 699 ★)](https://repo.masters-masters.com/en/ranks/b_movie) |
| View on GitHub | [MenglinLu/Chinese-clinical-NER](https://github.com/MenglinLu/Chinese-clinical-NER) |

## Repository

CCKS2019中文命名实体识别任务。从医疗文本中识别疾病和诊断、解剖部位、影像检查、实验室检验、手术和药物6种命名实体。现已实现基于jieba和AC自动机的baseline构建、基于BiLSTM和CRF的序列标住模型构建。bert的部分代码主要源于https://github.com/charles9n/bert-sklearn.git 感谢作者。 模型最终测试集得分0.81，还有较大改进空间。可以当做一个baseline。

The tier for repos with 250 to 699 ★. Published odds per card pulled: 20%. Rarity: Uncommon.

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Data: GitHub · Illustration drawn by RepoMasters from the repo’s ID and its language color.