从给定数据中抽出一份作为测试样本,对余下样本按照train : dev = 10 : 1的比例划分
BiLSTM+CRF
Linux+Python3.6+Keras2.2.4+Tensorflow1.11.0
线上分数=0.71
赛题第一个阶段建模为一个命名实体识别任务(NER),但是还没思考好第二阶段的方法。通过调研发现,实体识别和关系抽取两个子任务可以通过一个模型实现,比起前者,后者似乎更有趣一些。
This project forked from zhpmatrix/tianchi-ruijin
瑞金医院MMC人工智能辅助构建知识图谱大赛-baseline
从给定数据中抽出一份作为测试样本,对余下样本按照train : dev = 10 : 1的比例划分
BiLSTM+CRF
Linux+Python3.6+Keras2.2.4+Tensorflow1.11.0
线上分数=0.71
赛题第一个阶段建模为一个命名实体识别任务(NER),但是还没思考好第二阶段的方法。通过调研发现,实体识别和关系抽取两个子任务可以通过一个模型实现,比起前者,后者似乎更有趣一些。
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