[1] 黄恒琪,于娟,廖晓,等.知识图谱研究综述[J].计算机系统应用, 2019, 28(6):1-12. [2] 黄巍,徐海强.知识图谱在汽车维修领域的应用[J].信息技术与标准化, 2021(5):31-34. [3] 赵军.知识图谱[M].北京:高等教育出版社,2018. [4] 王尚.中草药文献知识抽取方法研究与应用[D].长春:吉林大学,2020. [5] CHINCHOR N, MARSH E. MUC-7 information extraction task definition[C]//Proceedings of the 7th message understanding conference. Stroudsburg:Association for Computational Linguistics,1998:359-367 [6] 李肖俊,邵必林.多源异构数据情境中学术知识图谱模型构建研究[J].现代情报, 2020,40(6):88-97. [7] 王传栋,徐娇,张永.实体关系抽取综述[J].计算机工程与应用, 2020, 56(12):25-36. [8] 刘峤,李杨,段宏,等.知识图谱构建技术综述[J].计算机研究与发展, 2016, 53(3):582-600. [9] ZHOU G, SU J, ZHANG J, et al. Exploring various knowledge in relation extraction[C]//Proceedings of the 43rd annual meeting on Association for Computational Linguistics. Stroudsburg:Association for Computational Linguistics,2005:427-434. [10] 郭喜跃,何婷婷,胡小华,等.基于句法语义特征的中文实体关系抽取[J].中文信息学报,2014,28(6):183-189. [11] FUNDEL K, KüFFNER R, ZIMMER R. RelEx——relation extraction using dependency parse trees[J].Bioinformatics,2007,23(3):365-371. [12] KAMBHATLA N. Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations[C]//Proceedings of the ACL on interactive poster and demonstration sessions. Stroudsburg:Association for Computational Linguistics,2004:22-26. [13] 刘克彬,李芳,刘磊,等.基于核函数中文关系自动抽取系统的实现[J].计算机研究与发展,2007(8):1406-1411. [14] ZELENKO D, AONE C, RICHARDELLA A. Kernel methods for relation extraction[J].Journal of machine learning research,2003,3(3):1083-1106. [15] 姚春华,刘潇,高弘毅,等.基于句法语义特征的实体关系抽取技术[J].通信技术,2018,51(8):1828-1835. [16] 鄂海红,张文静,肖思琪,等.深度学习实体关系抽取研究综述[J].软件学报,2019, 30(6):1793-1818. [17] 李枫林,柯佳.基于深度学习框架的实体关系抽取研究进展[J].情报科学,2018, 36(3):169-176. [18] ZHENG S, XU J, BAO H, et al. Joint learning of entity semantics and relation pattern for relation extraction[C]//Proceedings of the joint European conference on machine learning and knowledge discovery in databases. Cham:Springer-Verlag, 2016:443-458. [19] 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. [20] MIWA M, BANSAL M. End-to-end relation extraction using LSTMs on sequences and tree structures[EB/OL].[2022-05-01]. https://arxiv.org/abs/1601.00770. [21] ZHENG S, HAO Y, LU D, et al. Joint entity and relation extraction based on a hybrid neural network[J].Neurocomputing, 2017, 257(12):59-66. [22] SUN C Z, FENG W, HONG Y B, et al. Joint extraction of entities and relations based on a novel tagging scheme[EB/OL].[2022-05-01]. https://arxiv.org/abs/1706.05075. [23] WANG J, LU W. Two are better than one:joint entity and relation extraction with table-sequence encoder[C]//Proceedings of the 2020 conference on empirical methods in natural language processing. Stroudsburg:Association for Computational Linguistics, 2020:1706-1721. [24] 刘一斌.中医中文电子病历命名实体语料库构建及研究[D].广州:广州中医药大学,2020. [25] 高甦,金佩,张德政.基于深度学习的中医典籍命名实体识别研究[J].情报工程, 2019,5(1):113-123. [26] 卢克治.基于中医古籍的知识图谱构建与应用[D].北京:北京交通大学,2020. [27] 高佳奕,杨涛,董海艳,等.基于LSTM-CRF的中医医案症状命名实体抽取研究[J].中国中医药信息杂志,2021,28(5):20-24. [28] 梁科.面向中医医案的数据挖掘技术研究及应用[D].济南:山东大学,2016. [29] 魏尊强,舒红平,王亚强.基于序列标注的中医症状名识别技术研究[J].山东工业技术,2015(8):237-238. [30] 孟洪宇,孟庆刚.基于条件随机场的中医术语抽取方法及其应用探析[J].中华中医药学刊,2014,32(10):2334-2337. [31] 李明浩,刘忠,姚远哲.基于LSTM-CRF的中医医案症状术语识别[J].计算机应用,2018,38(S2):42-46. [32] 肖瑞,胡冯菊,裴卫.基于BiLSTM-CRF的中医文本命名实体识别[J].世界科学技术-中医药现代化,2020,22(7):2504-2510. [33] 王煜.面向医学文献的知识抽取关键技术研究[D].合肥:中国科学技术大学, 2021. [34] 庞震,顾继昱,吴宇飞.中医诊治高血压医疗实体提取问题研究[J].医学信息学杂志, 2021, 42(9):45-51. [35] 于彤,李敬华,朱玲,等.中医临床知识图谱的构建与应用[J].科技新时代,2017(4):51-54. [36] 吴小雪,张庆辉.预训练语言模型在中文电子病历命名实体识别上的应用[J].电子质量, 2020(9):61-65. [37] DEVLIN J, CHANG M W, LEE K, et al. BERT:pre-training of deep bidirectional transformers for language understanding[EB/OL].[2022-05-01]. https://arxiv.org/abs/1810.04805. [38] ZENG X, ZENG D, HE S, et al. Extracting relational facts by an end-to-end neural model with copy mechanism[C]//Proceedings of the 56th annual meeting of the Association for Computational Linguistics. Stroudsburg:Association for Computational Linguistics, 2018:506-514. |