[1] 沈思,孙豪,王东波.基于深度学习表示的医学主题语义相似度计算及知识发现研究[J].情报理论与实践,2020,43(5):183-190. [2] GAO Y, WANG Y, WANG P, et al. Medical named entity extraction from Chinese resident admit notes using character and word attention-enhanced neural network[J]. International journal of environmental research and public health, 2020, 17(5):1614. [3] ABIB M S, KALITA J. Scalable biomedical named entity recognition:investigation of a database-supported SVM approach[J]. International journal of bioinformatics research and applications, 2010, 6(2):191-208. [4] WEI Q, CHEN T, XU R, et al. Disease named entity recognition by combining conditional random fields and bidirectional recurrent neural networks[EB/OL].[2022-03-28].https://doi.org/10.1093/database/baw140. [5] NOZZA D, MANCHANDA P, FERSINI E, et al. LearningToAdapt with word embeddings:domain adaptation of named entity recognition systems[J]. Information processing&management, 2021, 58(3):102537. [6] PUCCETTI G, CHIARELLO F, FANTONI G. A simple and fast method for named entity context extraction from patents[J]. Expert systems with applications, 2021, 184:115570. [7] ZHANG J, HUANG W, JI D, et al. Globally normalized neural model for joint entity and event extraction[J]. Information processing&management, 2021, 58(5):102636. [8] HU Q, LIU N, WANG J, et al. An overlapping sequence tagging mechanism for symptoms and details extraction on Chinese medical records[J]. Computers&electrical engineering, 2021, 91:107019. [9] 陈德鑫,占袁圆,杨兵,等.基于CNN-BiLSTM模型的在线医疗实体抽取研究[J].图书情报工作,2019,63(12):105-113. [10] DEVLIN J, CHANG M W, LEE K, et al. Bert:pre-training of deep bidirectional transformers for language understanding[EB/OL].[2022-03-28].https://arxiv.org/pdf/1810.04805.pdf. [11] YANG X, BIAN J, HOGAN W R, et al. Clinical concept extraction using transformers[J]. Journal of the American Medical Informatics Association, 2020, 27(12):1935-1942. [12] LIU J, GAO L, GUO S, et al. A hybrid deep-learning approach for complex biochemical named entity recognition[J]. Knowledge-based systems, 2021, 221:106958. [13] 任秋彤,王昊,熊欣,等.融合GCN远距离约束的非遗戏剧术语抽取模型构建及其应用研究[J].数据分析与知识发现, 2021, 5(12):123-136. [14] 刘浏,秦天允,王东波.非物质文化遗产传统音乐术语自动抽取[J].数据分析与知识发现,2020,4(12):68-75. [15] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[EB/OL].[2022-03-28]. https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf. [16] LI Y, DU G, XIANG Y, et al. Towards Chinese clinical named entity recognition by dynamic embedding using domain-specific knowledge[J]. Journal of biomedical informatics, 2020, 106:103435. [17] 程齐凯,李鹏程,张国标,等.学术文本词汇功能识别——基于标题生成策略和注意力机制的问题方法抽取[J].情报学报,2021,40(1):43-52. [18] 范涛,王昊,张宝隆.基于远程监督和深度学习的非物质文化遗产文本属性抽取研究[J].情报理论与实践,2021,44(10):1-7. [19] LI X, ZHANG H, ZHOU X H. Chinese clinical named entity recognition with variant neural structures based on BERT methods[J]. Journal of biomedical informatics, 2020, 107:103422. [20] SRIVASTAVA R K, GREFF K, SCHMIDHUBER J. Highway networks[EB/OL].[2022-03-28]. https://arxiv.org/pdf/1505.00387. [21] ALIYUN. A labeled Chinese dataset for diabetes[EB/OL].[2022-03-28].https://tianchi.aliyun.com/competition/entrance/231687/information. [22] 李旭晖,程威,唐小雅,等.基于多层卷积神经网络的金融事件联合抽取方法[J].图书情报工作,2021,65(24):89-99. [23] 杜悦,王东波,江川,等.数字人文下的典籍深度学习实体自动识别模型构建及应用研究[J].图书情报工作,2021,65(3):100-108. [24] 俞琰,陈磊,姜金德,等.融合论文关键词知识的专利术语抽取方法[J].图书情报工作,2020, 65(14):104-111. [25] 何春辉,王梦贤,何小波.基于双层Bi-LSTM-CRF模型的糖尿病领域命名实体识别[J].邵阳学院学报(自然科学版),2020,17(1):21-26. [26] DENG J, CHENG L, WANG Z. Self-attention-based BiGRU and capsule network for named entity recognition[EB/OL].[2022-03-28].https://arxiv.org/pdf/2002.00735. [27] 杨佳琦.基于中文自然语言处理的糖尿病知识图谱构建[D].包头:内蒙古科技大学,2020. [28] WANG Y, SUN Y, MA Z, et al. Named entity recognition in Chinese medical literature using pretraining models[J]. Scientific programming, 2020, 2020:8812754.作者贡献说明:韩普:提出研究思路,对研究方法提供指导,撰写论文,修改论文;顾亮:采集数据,编写代码,撰写论文,修改论文。 |