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中文医学知识图谱命名实体识别,包括bi-LSTM+CRF,transformer+CRF等模型

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pumpkinduo/KnowledgeGraph_NER

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medical_NER

本项目数据集来自ccks2017任务二,任务是中文病例的命名实体识别

数据处理后主要类标

"O" "B-body" "I-body" "E-body" "B-symp" "I-symp" "E-symp" "B-dise" "I-dise" "E-dise" "B-chec" "I-chec" "E-chec" "B-cure" "I-cure" "E-cure"

模型结构

采用bi-LSTM+CRF/transformer+CRF,此后会对模型进行优化,数据根据需求处理。
1.dataset文件夹 原始数据
2.LSTM_CRF.py bi-LSTM模型
3.data_util.py 数据处理
4.train.py main
5.transformer_CRF transformer模型

Requirements

python 3
tensorflow 1.12

评价指标

microF1 打分函数precision_recall_fscore_support

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中文医学知识图谱命名实体识别,包括bi-LSTM+CRF,transformer+CRF等模型

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