[1] 王效岳,白如江.海量网络学术文献自动分类技术研究[M].北京:人民出版社,2015:40-42.
[2] SCHANKERMAN M, PAKES A. Estimates of the value of patent rights in European countries during the post-1950 period[J]. Economic journal, 1986, 96(384):1052-1076.
[3] 许海云,岳増慧,雷炳旭,等.基于专利技术功效主题词与专利引文共现的核心专利挖掘[J].图书情报工作,2014,58(4):59-64.
[4] 袁润,钱过.识别核心专利的粗糙集理论模型[J].图书情报工作, 2015,59(2):123-130.
[5] 马永涛,张旭,傅俊英,等.核心专利及其识别方法综述[J].情报杂志,2014,33(5):38-43,70.
[6] KWON O, SEO J, NOH K, et al. Categorizing influential patents using bibliometric analysis of patent citations network[J]. Information-an international interdisciplinary journal, 2007, 10(3):313-326.
[7] CHOI C, PARK Y. Monitoring the organic structure of technology based on the patent development paths[J]. Technological forecasting and social change, 2009, 76(6):754-768.
[8] HSU C W, CHANG P L, HSIUNG CM, et.al. Charting the evolution of biohydrogen production technology through a patent analysis[J]. Biomass & bioenergy, 2015,76(5):1-10.
[9] 张欣,马瑞敏.基于改进PageRank算法的核心专利发现研究[J].图书情报工作,2018,62(10):106-115.
[10] 亢川博,王伟,穆晓敏,等.核心专利识别的综合价值模型[J].情报科学,2018,36(2):67-70.
[11] WANG Y, BAI H J, STANTON M, et al. PLDA:parallel latent Dirichlet allocation for large-scale applications[C]//International conference on algorithmic aspects in information and management. San Francisco:Springer-verlag, 2009:301-314.
[12] NEWMAN M E J,GIRVAN M. Finding and evaluating community structure in networks[J].Physical review, 2004,69(2):108-113.
[13] BLONDEL V D, GUILLAUME J L, LAMBIOTTE R, et al. Fast unfolding of communities in large networks[J]. Journal of statistical mechanics:theory and experiment,2008,30(2):155-168
[14] LEE Y G, SONG Y I. Selecting the key research areas in nano-technology field using technology cluster analysis:a case study based on National R&D Programs in South Korea[J].Technovation, 2007, 27(12):57-64.
[15] 栾春娟,曾国屏.基于SNA核心技术领域测度研究[J].图书情报工作,2011,55(6):33-35.
[16] 范宇,符红光,文奕.基于LDA模型的专利信息聚类技术[J].计算机应用,2013,33(S1):87-89,93.
[17] 李佳佳,马铁驹.基于专利数据的风能核心技术识别及趋势分析[J].科技管理研究,2017(12):129-136.
[18] 伊惠芳,吴红,马永新,等. 基于LDA和战略坐标的专利技术主题分析——以石墨烯领域为例[J].情报杂志,2018,37(5):97-102.
[19] BLEI D M, NG A Y, JORDAN M I. Latent Dirichlet allocation[J].Journal of machine learning research,2003(3):993-1022.
[20] BLEI D M, LAFFERTY J. Dynamic topic models[C]//Proceedings of the 23rd international conference on machine learning. NewYork:ACM,2006:113-120.
[21] WANG X, MCCALLUM A, WEI X. Topical N-Grams:phrase and topic discovery, with an application to information retrieval[C]//IEEE international conference on data mining. Omaha:IEEE Computer Society, 2007:697-702.
[22] ELKISHKY A, SONG Y, VOSS C R, et al. Scalable topical phrase mining from text corpora[J]. Proceedings of the VLDB endowment, 2014, 8(3):305-316.
[23] 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.
[24] SIEVERT C, SHIRLEY K. LDAvis:a method for visualizing and interpreting topics[C]//Proceedings of the workshop on interactive language learning, visualization, and interfaces. Baltimore:Association for Computational Linguistics, 2014:63-70.
[25] 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.
[26] 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.
[27] LANDAUER T K, DUMAIS S T. A solution to Plato's problem:the latent semantic analysis theory of acquisition, induction, and representation of knowledge[J]. Psychological review, 1997, 104(2):211-240.
[28] SHEN C, LI T, DING C H Q. Integrating clustering and multi-document summarization by bi-mixture probabilistic latent semantic analysis (PLSA) with sentence bases[C]//AAAI conference on artificial intelligence. San Francisco:AAAI Press, 2011:914-920.
[29] BONACICH P B. Factoring and weighting approaches to status scores and clique identification[J].Journal of mathematical sociology,1972, 2(1):113-120.
[30] STURROCK K, ROCHA J. A multidimensional scaling stress evaluation table[J]. Field methods, 2016, 12(1):49-60. |