kyzhouhzau / Clinical Ner
Licence: apache-2.0
面向中文电子病历的命名实体识别
Stars: ✭ 151
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运行流程：
@Author zhoukaiyin
任务描述
本评测任务为面向中文电子病历的命名实体识别，即对于给定的一组电子病历纯文本文档，任务的目标是识别并抽取出与医学临床相关的实体提及（entity mention），并将它们归类到预先定义好的类别（pre-defined categories），比如症状，药品，手术等。
第一步：数据处理(Linux)
$python raw2bio.py -1 #将训练数据分词并贴上字典特征
$python raw2bio.py -2 #将标签数据分词并贴上标签
$python raw2bio.py -3 #将标签保存成pickle文件为了后面将训练数据与标签合在一起
$python raw2bio.py -4 #将标签与训练数据文本接起来构成如下格式
$python raw2bio.py -1 test #将测试数据处理成需要的格式
第二部：模型训练(Linux)
$bash wapiti_ccks.sh #训练模型，模型储存在/eval/bio_ccks中
第三部分：获得结果(Linux)
$python get_result.py #提取结果文件，结果保存在CCKS_result中其格式为BIO和finall中格式为官方标签格式
$python onefile.py #将结果转成提交格式
结果文件
Flyon\CCKS_CRF\eval\result.txt
Wapiti is a simple and fast discriminative sequence labeling toolkit ( http://wapiti.limsi.fr )。A little same as CRF++
注：可以尝试BERT,ALBERT等预训练模型 参见：NLPGNN
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