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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (12): 61-69    DOI: 10.11925/infotech.2096-3467.2019.0684
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Identifying Entities of Online Questions from Cancer Patients Based on Transfer Learning
Meishan Chen,Chenxi Xia()
School of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan 430073, China
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[Objective] This study utilizes annotated corpus with a pre-trained model, aiming to identify entities from corpus of limited annotation. [Methods] First, we collected online questions from patients with lung or liver cancers. Then we developed a KNN-BERT-BiLSTM-CRF framework combining instance and parameter transfer, which recognized named entities with small amount of labeled data. [Results] When the k value of instance-transfer was set to 3, we achieved the best performance of named entity recognition. Its F value was 96.10%, which was 1.98% higher than the performance of models with no instance-transfer techniques. [Limitations] The proposed method needs to be examined with entities of other diseases. [Conclusions] The cross-domain transfer learning method could improve the performance of entity identification.

Key wordsBERT      BiLSTM      Named Entity Recognition      Transfer Learning     
Received: 14 June 2019      Published: 25 December 2019
ZTFLH:  TP391  
Corresponding Authors: Chenxi Xia     E-mail:

Cite this article:

Meishan Chen,Chenxi Xia. Identifying Entities of Online Questions from Cancer Patients Based on Transfer Learning. Data Analysis and Knowledge Discovery, 2019, 3(12): 61-69.

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实体类型 简洁定义 例子 目标领域标注数量 源领域标注数量
身体部位 包括器官, 身体部位和组织 头部, 颈部 1 359 6 876
细胞实体 包括细胞、分子或细胞层面的解剖实体 血红蛋白,巨细胞 130 398
诊断程序 包括用于诊断的检测和活检程序 活检, CT, b超, 铁含量 156 1 102
药物 包括用于治疗目的的物质 华蟾素胶囊, 吗啡 259 1 805
度量 一个命名实体的核心属性, 如药物的剂量 10 mg, 2% 78 257
个体 包括个人(性别、年龄等)和人口群体 父亲, 女性, 16岁 1 188 2 506
问题 包括疾病、症状、异常和并发症 疼痛, 破裂, 肺癌, 肿瘤 4 975 25 427
治疗程序 指程序或医学、设备用于治疗以及未指明的植入预防手术干预 肾镜切除, 植入, 化疗 1 003 4 169
癌症分期 决定癌症发展与扩散程度的方法 早期, 前期, 晚期 1 142 4 304
名称 类型 数量(句) 标注情况
源领域数据集 肺癌 11 822 有标注
目标领域数据集 肝癌 2 000 有标注
网络层 参数 取值
Doc2Vec 算法 DM
窗口大小 5
最小词频 5
学习率 由0.025递减至0.001
向量维度 100
BERT 批处理大小 32
学习率 2e-5
样本最大长度 128
迭代次数 10
优化方法 Adam
BiLSTM L2正则化 0.001
迭代次数 10
Dropout 0.5
Word2Vec 算法 Skip-gram
窗口大小 5
学习率 由0.025递减至0.001
最小词频 3
向量维度 100
模型 P(%) R(%) F(%)
Word2Vec-BiLSTM-CRF 85.98 86.55 86.26
BERT-BiLSTM-CRF 92.91 95.36 94.12
模型 评价
k=0 k=1 k=2 k=3 k=4 k=5 k=6
P 92.91 93.54 94.89 95.74 95.40 94.73 94.60
R 95.36 95.74 96.51 96.75 96.24 96.30 95.68
F 94.12 94.63 95.69 96.10 95.82 95.51 95.14
KNN-Word2Vec-BiLSTM-CRF P 85.98 88.73 90.45 91.48 91.65 91.03 90.77
R 86.55 89.57 91.30 92.48 92.62 92.05 91.90
F 86.26 89.15 90.87 91.98 92.13 91.54 91.33
模型 P(%) R(%) F(%)
Word2Vec-BiLSTM-CRF 85.98 86.55 86.26
KNN-Word2Vec-BiLSTM-CRF(k=4) 91.65 92.62 92.13
BERT-BiLSTM-CRF 92.91 95.36 94.12
KNN-BERT-BiLSTM-CRF(k=3) 95.47 96.75 96.10
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