[1] 王效岳, 刘自强, 白如江, 等. 基于基金项目数据的研究前沿主题探测方法[J]. 图书情报工作, 2017, 61(13):87-98. (WANG X Y, LIU Z Q, BAI R J, et al. The method of research front topic detection based on the fund project data[J]. Library and information service, 2017, 61(13):87-98.)
[2] ALLAN J, CARBONELL J, DODDINGTON G, et al. Topic detection and tracking pilot study final report[C]//Proceedings of the DARPA broadcast news transcription and understanding workshop. Virginia:Lansdowne, 1998:194-218.
[3] 张婧, 刘彦君, 张炜, 等. 基于科研项目数据的科技前沿识别有效路径实证探索[J]. 科技管理研究, 2019, 39(16):108-119. (ZHANG J, LIU Y J, ZHANG W, et al. Empirical exploration on effective paths to identify frontier tech based upon data of scientific research projects[J]. Science and technology management research, 2019, 39(16):108-119.)
[4] 李荣, 刘静, 李梦辉, 等. 基于基金项目数据的人工智能技术前沿性测度研究——技术创新决策视角分析[J]. 情报杂志, 2020, 39(9):81-87. (LI R, LIU J, LI M H, et al. Frontier measurement of artificial intelligence technology:analysis based on fund project data from the perspective of technology innovation decision-making[J]. Journal of intelligence, 2020, 39(9):81-87.)
[5] 贾佳. 全球人工智能及交叉领域:基于学科布局及热点研究、潜在研究趋势的分析[J]. 科学观察, 2021, 16(5):31-52. (JIA J. Global AI and interdisciplinary fields:analysis based on discipline layout, hot research and potential research trend[J]. Science focus, 2021, 16(5):31-52.)
[6] 王文娟, 马建霞. 基于LDA的科研项目主题挖掘与演化分析——以NSF海洋酸化研究为例[J]. 情报杂志, 2017, 36(7):34-39. (WANG W J, MA J X. Topic detection and evolution analysis of research project based on LDA:a case study of projects on ocean acidification supported by NSF[J]. Journal of intelligence, 2017, 36(7):34-39.)
[7] LIMA I, RHEUBANM J. Topics and trends in NSF ocean sciences awards[J]. Oceanography 2018, 31(4):164-170.
[8] 刘自强, 岳丽欣, 朱承宁, 等. 基金项目和论文主题扩散演化路径识别及其可视化研究[J]. 现代情报, 2022, 42(11):76-86. (LIU Z Q, YUE L X, ZHU C N, et al. Research on topic diffusion evolution path identification and visualization of fund projects and papers[J]. Journal of modern information, 2022, 42(11):76- 86.)
[9] 静发冲, 李晨英, 韩明杰, 等. 基于文本挖掘的美国NSF生物科学部新兴前沿项目主题分析[J]. 现代情报, 2014, 34(12):107-112. (JING F C, LI C Y, HAN M J, et al. Topic analysis of projects from emerging frontiers division of NSF's directorate for biological science based on text mining[J]. Journal of modern information, 2014, 34(12):107-112.)
[10] 靳嘉林, 王曰芬, 巴志超, 等. 基金项目研究的主题挖掘与动态演化分析——以美国NSF数据中AI领域为例[J]. 情报学报, 2022, 41(9):967-979. (JIN J L, WANG Y F, BA Z C, et al. Topic mining and dynamic evolution analysis of funding projects:case studies of AI field in NSF data[J]. Journal of the China Society for Scientific and Technical Information, 2022, 41(9):967-979.)
[11] WANG Q. A bibliometric model for identifying emerging research topics[J]. Journal of the Association for Information Science and Technology, 2018, 69:290-304.
[12] 徐路路, 王效岳, 白如江, 等. 基于DTM模型和文本特征分析的基金项目新兴趋势探测研究——以NSF石墨烯领域为例[J]. 数据分析与知识发现, 2018, 2(3):87-97. (XU L L, WANG X Y, BAI R J, et al. Detecting emerging trends of funds based on DTM model and text analytics:case study of NSF graphene field[J]. Data analysis and knowledge discovery, 2018, 2(3):87-97.)
[13] 李静, 徐路路, 赵素君. 基于时间序列分析和SVM模型的基金项目新兴主题趋势预测与可视化研究[J]. 情报理论与实践, 2019, 42(1):118-123. (LI J, XU L L, ZHAO S J. Prediction and visualization of emerging topics of fund sponsored projects based on time series analysis and SVM model[J]. Information studies:theory & application, 2019, 42(1):118-123.)
[14] 范丽鹏, 王曰芬, 岑咏华, 等. 基金项目计划学部交叉及对前沿分布的影响研究——以美国NSF数据中AI领域为例[J]. 情报学报, 2022, 41(9):956-966. (FAN L P, WANG Y F, CEN Y H, et al. Frontier influence of the inter-directorate in project planning:case studies of AI field in NSF data[J]. Journal of the China Society for Scientific and Technical Information, 2022, 41(9):956-966.)
[15] 陈稳, 陈伟. 基于计量指标多变量LSTM模型的新兴主题热度预测研究[J]. 数据分析与知识发现, 2022, 6(10):35-45. (CHEN W, CHEN W. Predicting popularity of emerging topics with multivariable LSTM and bibliometric indicators[J]. Data analysis and knowledge discovery, 2022, 6(10):35-45.)
[16] 许海云, 董坤, 刘昊, 等. 基于异构网络的学科交叉主题发现方法[J]. 情报科学, 2017, 35(6):130-137. (XU H Y, DONG K, LIU H, et al. Interdisciplinary topics discovery method based on heterogeneous networks[J]. Information science, 2017, 35(6):130-137.)
[17] 张斌. 交叉学科主题探究:从主题聚类视角[J]. 情报科学, 2020, 38(10):49-55. (ZHANG B. Interdisciplinary subject exploration:from the perspective of topic clustering[J]. Information science, 2020, 38(10):49-55.)
[18] 阮光册, 夏磊. 学科间交叉研究主题识别——以图书情报学与教育学为例[J]. 情报科学, 2020, 38(12):152-157. (RUAN G C, XIA L. Research on interdisciplinary topics identification:a case study of library & information science and education[J]. Information science, 2020, 38(12):152-157.)
[19] 韩正琪, 刘小平, 寇晶晶. 基于Rao-Stirling指数和LDA模型 的领 域学 科交 叉主 题识 别——以纳 米科 技为 例[J]. 情报 科学, 2020, 38(2):116-124. (HAN Z Q, LIU X P, KOU J J. Interdisciplinary literature discovery based on RaoStirling diversity indices:case studies in nanoscience and nanotechnology[J]. Information science, 2020, 38(2):116-124.)
[20] 韩芳, 张生太, 冯凌子, 等. 基于专利文献技术融合测度的突破性创新主题识别——以太阳能光伏领域为例[J]. 数据分析与知识发现, 2021, 5(12):137-147. (HAN F, ZHANG S T, FENG L Z, et al. Identifying breakthrough patent topics by measuring technological convergence:case study of solar PV domain[J]. Data analysis and knowledge discovery, 2021, 5(12):137-147.)
[21] HE T, FU W, XU J, et al. Discovering interdisciplinary research based on neural networks[J]. Frontiers in bioengineering and biotechnology, 2022, 10:908733.
[22] NICHOLS L. A topic model approach to measuring interdisciplinarity at the National Science Foundation[J]. Scientometrics, 2014, 100(3):741-754.
[23] BROMHAM L, DINNAGE R, HUA X. Interdisciplinary research has consistently lower funding success[J]. Nature, 2016, 534(7609):684-687.
[24] 张雪, 张志强. 美国科学基金会资助项目的学科交叉度演化规律及影响研究[J]. 情报理论与实践, 2021, 44(12):122-132. (ZHANG X, ZHANG Z Q. Research on evolution and influence of interdisciplinarity in NSF funded projects[J]. Information studies:theory & application, 2021, 44(12):122-132.)
[25] 温芳芳, 杨倩倩, 李翔宇. 我国人文社会科学学科交叉性的测度及其演化规律研究——基于国家社科基金关键词耦合分析[J]. 现代情报, 2022, 42(3):157-167. (WEN F F, YANG Q Q, LI X Y. Research on the interdisciplinary measurement and evolution of Chinese social science and humanity:based on the keywords coupling analysis of the national social science fund projects[J]. Journal of modern information, 2022, 42(3):157-167.)
[26] 王卫军, 姚畅, 乔子越, 等. 基于词嵌入的国家自然科学基金学科交叉知识发现方法——以"人工智能"与"信息管理"为例[J]. 情报学报, 2021, 40(8):831-845. (WANG W J, YAO C, QIAO Z Y, et al. Method of discovering interdisciplinary knowledge of the national natural science foundation of China based on word embedding:a case study on artificial intelligence and information management[J]. Journal of the China Society for Scientific and Technical Information, 2021, 40(8):831-845.)
[27] 杨金庆, 张力. 学科交叉视角下新兴主题识别特征分析——以医学信息学为例[J]. 情报工程, 2021, 7(4):3-12. (YANG J Q, ZHANG L. The characteristics analysis of emerging topic identification from interdisciplinary perspective:a case study of medical informatics[J]. Technology intelligence engineering, 2021, 7(4):3-12.)
[28] 刘冬东. 新兴交叉主题识别与引文扩散影响研究——以计算生物学领域为例[D]. 郑州:华北水利水电大学, 2020. (LIU D D. The identification and citation diffusion impact of the emerging interdisciplinary topics:a case study of computational biology the identification and citation diffusion impact of the emerging[D]. Zhengzhou:North China University of Water Resources and Electric Power, 2020.)
[29] 张琳, 黄颖. 交叉科学测度、评价与应用[M]. 北京:科学出版社, 2019. (ZHANG L, HUANG Y. Interdisciplinary measurement, evaluation and application[M]. Beijing:China Science Publishing & Media, 2019.)
[30] ZHANG L, ROUSSEAU R, GLANZEL W. Diversity of references as an indicator of the interdisciplinarity of journals:taking similarity between subject fields into account[J]. Journal of the Association for Information Science and Technology, 2016, 67(5):1257-1265.
[31] BLEI D, NG A, JORDAN M. Latent Dirichlet allocation[J]. The journal of machine learning research, 2003(3):993-1022.
[32] 范云满, 马建霞. 基于LDA与新兴主题特征分析的新兴主题探测研究[J]. 情报学报, 2014, 33(7):698-711. (FAN Y M, MA J X. Detection of emerging topics based on LDA and feature analysis of emerging topics[J]. Journal of the China Society for Scientific and Technical Information, 2014, 33(7):698-711.)
[33] 白敬毅, 颜端武, 陈琼. 基于主题模型和曲线拟合的新兴主题趋势预测研究[J]. 情报理论与实践, 2020, 43(7):130-136. (BAI J Y, YAN D W, CHEN Q. Trend prediction of emerging topics based on topic model and curve fitting[J]. Information studies:theory & application, 2020, 43(7):130-136.)
[34] MANN G, MIMNO D, MCCALLUM A. Bibliometric impact measures leveraging topic analysis[C]//Proceedings of the 6th ACM/IEEE-CS joint conference on digital libraries. Chapel Hill:ACM, 2006:65-74.
[35] SESKIR Z, AYDINOGLU A. The landscape of academic literature in quantum technologies[J]. International journal of quantum information, 2021, 19(2):2150012.
[36] 刘自强, 许海云, 岳丽欣, 等. 面向研究前沿预测的主题扩散演化滞后效应研究[J]. 情报学报, 2018, 37(10):979-988. (LIU Z Q, XU H Y, YUE L X, et al. Research on lagging effect of topic diffusion evolution face to prediction of research front[J]. Journal of the China Society for Scientific and Technical Information, 2018, 37(10):979-988.)
[37] LEYDESDORFFL. A method for generating overlay maps on the basis of aggregated journal-journal citation relations in 2015[EB/OL].[2023-04-02]. http://www.leydesdorff.net/wc15/wc19/index.htm.