[1] 温军, 张森. 专利、技术创新与经济增长[J]. 华东经济管理, 2019,33(8):152-158.
[2] SCHMOOKLER J. Changes in industry and in the state of knowledge as determinants of industrial invention[C]//NELSON R R. The rate and direction of inventive activity. Princeton:Princeton University Press, 1962:195-232.
[3] GRILICHES Z. Patent statistics as economic indicators:a survey[J]. Journal of economic literature, 1990, 28(4):1661-1707.
[4] SCHMOCH U. Indicators and the relations between science and technology[J]. Scientometrics, 1997, 38(1):103-116.
[5] OECD. Patent statistics manual[M]. Paris:OECD Publishing, 2009.
[6] 赵阳, 文庭孝. 专利技术信息挖掘研究进展[J]. 图书馆, 2018(4):28-33.
[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] 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.
[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] 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.
[11] NEWMAN M E J, GIRVAN M. Finding and evaluating community structure in networks[J]. Physical review, 2004, 69(2):108-113.
[12] 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.
[13] HAYOUNG C, SEUNGHYUN O, SUNGCHUL C, et al. Innovation topic analysis of technology:the case of augmented reality patents[J]. IEEE access, 2018(6):16119-16137.
[14] 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.
[15] 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.
[16] 吕晓蓉. 专利价值评估指标体系与专利技术质量评价实证研究[J]. 科技进步与对策, 2014, 31(20):113-115.
[17] LANJOUW J, SHANKERMAN M. Stylized facts of patent litigation:value, scopeand ownership[R/OL].[2019-10-19]. https://www.nber.org/papers/w6297.pdf.
[18] 孙伟, 刘文静, 葛丽阁, 等. 一种基于词加权LDA模型的专利文献分类方法[J]. 计算机技术与发展, 2019(3):23-29.
[19] 庞剑锋, 卜东波, 白硕. 基于向量空间模型的文本自动分类系统的研究与实现[J]. 计算机应用研究, 2001(9):23-26.
[20] BLEI D M, NG A Y, JORDAN M I. Latent Dirichlet allocation[J]. Journal of machine learning research, 2003(3):993-1022.
[21] 张晗, 徐硕, 乔晓东. 融合科技文献内外部特征的主题模型发展综述[J]. 情报学报, 2014, 33(10):1108-1120.
[22] 廖列法, 勒孚刚, 朱亚兰. LDA模型在专利文本分类中的应用[J]. 现代情报, 2017, 37(3):35-39.
[23] 杨超, 朱东华, 汪雪锋. 专利技术主题分析——基于SAO结构的LDA主题模型方法[J]. 图书情报工作, 2017, 61(3):86-96.
[24] WON S L, EUN J H, SO Y S. Predicting the pattern of technology convergence using big-data on large-scale triadic patents[J]. Technological forecasting & social change, 2015, 100:317-329.
[25] 张文君, 顾行发, 陈良富, 等. 基于均值-标准差的K均值初始聚类中心选取算法[J]. 遥感学报, 2006, 10(5):715-721.
[26] PAOLA D R, SABRINA S, VINCENZO L. A semantic-grained perspective of latent knowledge modeling[J]. Information fusion, 2017, 36:52-67.
[27] 李清海, 刘洋, 吴泗宗, 等. 专利价值评价指标概述及层次分析[J]. 科学学研究, 2007, 25(2):281-286.
[28] 严明义. 函数性数据的统计分析:思想、方法和应用[J]. 统计研究, 2007(2):87-94.
[29] 温颖, 周昕, 赵文明. 高职软件专业学生职业素养量化评价[J].计算机工程与设计, 2017, 38(9):2586-2590.
[30] BIRD S, KLEIN E, LOPER E. Natural language processing with python[M]. New York:O'Reilly Media Press, 2009:41-134.
[31] PEDREGOSA F, VAROQUAUX G. Scikit-learn:machine learning in python[J]. Journal of machine learning research, 2011, 12:2825-2830.
[32] LDA Developers. LDA:topic modeling with latent dirichlet allocation[EB/OL].[2019-11-22].https://lda.readthedocs.io/en/latest/.
[33] 国家知识产权局-国际专利分类表(2008.01版)[S/OL].[2019-09-01]. http://www.sipo.gov.cn/wxfw/zlwxxxggfw/zsyd/bzyfl/gjzlfl/201406/t20140630_973352.html.
[34] FRANK H, JOCHEN G, MICHAEL H. Memetic search for overlapping topics based on a local evaluation of link communities[J]. Scientometrics, 2016, 11(2):1089-1118.
[35] 张百尚, 商惠敏. 国内外芯片产业技术现状与趋势分析[J]. 科技管理研究, 2019(17):131-134.
[36] 王立娜, 唐川, 房俊民, 等. 2018年全球半导体领域规划与发展态势分析[J]. 世界科技研究与发展, 2019, 41(2):120-126. |