[1] 罗文馨,王园园.技术主题演化研究方法综述[J].知识管理论坛,2018,3(5):255-265.
[2] HUMMON N P, DEREIAN P. Connectivity in a citation network:the development of DNA theory[J]. Social networks, 1989, 11(1):39-63.
[3] MARTINELLI A. An emerging paradigm or just another trajectory? Understanding the nature of technological changes using engineering heuristics in the telecommunications switching industry[J]. Research policy, 2012, 41(2):414-429.
[4] LU L Y Y, LIU J S. A survey of intellectual property rights literature from 1971 to 2012:the main path analysis[C]//Proceedings of PICMET'14 conference:portland international center for management of engineering and technology; infrastructure and service integration. Piscataway:IEEE, 2014:1274-1280.
[5] PILKINGTON A, MEREDITH J. The evolution of the intellectual structure of operations management-1980-2006:a citation/co-citation analysis[J]. Journal of operations management, 2009, 27(3):185-202.
[6] LAI R J, LI M F. Technology evolution of lower extremity exoskeleton from the patent perspective[J].Key engineering materials. 2014, 625:536-541.
[7] WANG Z Y, LI G, LI C Y, et al. Research on the semantic-based co-word analysis[J]. Scientometrics,2012,90(3):855-875.
[8] 胡正银,刘春江,隗玲,等.面向TRIZ的领域专利技术挖掘系统设计与实践[J].图书情报工作,2017,61(1):117-124.
[9] BLEI D M, NG A Y, JORDAN M I. Latent dirichlet allocation[J]. Journal of machine learning research, 2003, 3(1):993-1022.
[10] 范少萍,安新颖,单连慧,等.基于医学文献的主题演化类型与演化路径识别方法研究[J].情报理论与实践,2019,42(3):114-119.
[11] BLEI D M, LAFFERTY J D. Dynamic topic models[C]//Proceedings of the 23rd international conference on Machine learning. New York:ACM, 2006:113-120.
[12] WANG X, MCCALLUM A. Topics over time:a non-Markov continuous-time model of topical trends[C]//Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. New York:ACM, 2006:424-433.
[13] PORTEOUS I, NEWMAN D, IHLER A, et al. Fast collapsed gibbs sampling for latent dirichlet allocation[C]//Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. New York:ACM, 2008:569-577.
[14] HOFFMAN M, BACH F R, BLEI D M. Online learning for latent dirichlet allocation[C]//Advances in neural information processing systems 23.Vancouver:Curran Associates Inc., 2010:856-864.
[15] GRIFFITHS T L, JORDAN M I, TENENBAUM J B, et al. Hierarchical topic models and the nested Chinese restaurant process[C]//Advances in neural information processing systems. Vancouver:ACM,2004:17-24.
[16] MIMNO D, WALLACH H M, TALLEY E, et al. Optimizing semantic coherence in topic models[C]//Proceedings of the conference on empirical methods in natural language processing. Edinburgh:Association for Computational Linguistics, 2011:262-272.
[17] 王婷婷,韩满,王宇.LDA模型的优化及其主题数量选择研究——以科技文献为例[J].数据分析与知识发现,2018,2(1):29-40.
[18] WANG X, MCCALLUMA A, WEI X. Topical n-grams:Phrase and topic discovery, with an application to information retrieval[C]//IEEE International Conference on Data Mining. Piscataway:IEEE,2007:697-702.
[19] LI B, WANG B, ZHOU R, et al. CITPM:A cluster-based iterative topical phrase mining framework[C]//International conference on database systems for advanced applications. Dallas:Springer International Publishing,2016:197-213.
[20] 张琴,张智雄.基于PhraseLDA模型的主题短语挖掘方法研究[J].图书情报工作,2017,61(8):120-125.
[21] 刘自强,许海云,岳丽欣,等.基于Chunk-LDAvis的核心技术主题识别方法研究[J].图书情报工作,2019,63(9):73-84.
[22] 孙孟孟. 基于名词短语提取与词条权重分析的话题提取算法研究[D].杭州:浙江大学,2014.
[23] GRAVES A. Supervised sequence labelling[M]//Supervised Sequence Labelling with Recurrent Neural Networks. Berlin:Springer, 2012:5-13.
[24] TAYLOR S J, LETHAM B. Forecasting at scale[J]. The American statistician, 2018, 72(1):37-45. |