[1] ZHA X, CHEN M. Study on early warning of competitive technical intelligence based on the patent map[J]. Journal of computers, 2010, 5(2):274-281.
[2] AN X, LI J, XU S, et al. An improved patent similarity measurement based on entities and semantic relations[J]. Journal of informetrics, 2021, 15(2):101135.
[3] ARTHUR W B. The nature of technology:what it is and how it evolves[M]. New York:Simon and Schuster, 2009.
[4] XU S, HAO L, YANG G, et al. A topic models based framework for detecting and forecasting emerging technologies[J]. Technology forecasting&social change, 2021, 162:120366.
[5] XU S, HAO L, AN X, et al. Emerging research topics detecting with multiple machine learning models[J]. Journal of informetrics, 2019, 13(4):100983.
[6] FLEMING L. Recombinant uncertainty in technological search[J]. Management science, 2001, 47(1):117-132.
[7] BALSMEIER B, ASSAF M, CHESEBRO, et al. Machine learning and natural language processing on the patent corpus:data, tools, and new measures[J]. Journal of economics&management strategy, 2018, 27(3):535-553.
[8] ARTS S, HOU J, GOMEZ J C. Natural language processing to identify the creation and impact of new technologies in patent text:code, data, and new measures[J]. Research policy, 2020, 50(2):1-13.
[9] SCHOENMAKERS W, DUYSTERS G. The technological origins of radical inventions[J]. Research policy, 2010, 39(8):1051-1059.
[10] SUN B, KOLESNIKOV S, GOLDSTEIN A, et al. A dynamic approach for identifying technological breakthroughs with an application in solar photovoltaics[J]. Technological forecasting&social change, 2021, 165:120534.
[11] BOWER J L, CHRISTENSEN C M. Disruptive technologies:catching the wave[J]. Harvard business review, 1995, 73(1):43-53.
[12] CHRISTENSEN C M. The innovator's dilemma:when new technologies cause great firms to fail[M]. Boston:Harvard Business School Press, 1997.
[13] GOVINDARAJAN V, KOPALLE P. The usefulness of measuring disruptiveness of innovations ex post in making ex ante predictions[J]. Journal of product innovation management, 2006, 23(1):12-18.
[14] SOOD A, TELLIS G J. Demystifying disruption:a new model for understanding and predicting disruptive technologies[J]. Marketing science, 2011, 30(2):339-354.
[15] 周洋,张庆普.高端颠覆性创新的技术演进轨迹和市场扩散路径[J].研究与发展管理, 2017, 29(6):99-108.(ZHOU Y, ZHANG Q P. Technology evolution map and market diffusion path of high-end disruptive innovation[J]. R&D management, 2017, 29(6):99-108.)
[16] SUSENO Y. Disruptive innovation and the creation of social capital in Indonesia's urban communities[J]. Asia pacific business review, 2018, 24(2):174-195.
[17] DOWNES L, NUNES P. Big bang disruption:strategy in the age of devastating innovation[M]. London:Portfolio, 2014.
[18] 苏成,赵志耘,赵筱媛,等.颠覆性技术新阐释:概念、内涵及特征[J].情报学报, 2021, 40(12):1253-1262.(SU C, ZHAO Z Y, ZHAO X Y, et al. New explanation on disruptive technology:concept, connotation and features[J]. Journal of the China Society for Scientific and Technical Information, 2021, 40(12):1253-1262.)
[19] 张佳维,董瑜.颠覆性技术识别指标的研究进展[J].情报理论与实践, 2020, 43(6):194-199, 193.(ZHANG J W, DONG Y. Research progress of disruptive technical identification indicators[J]. Information studies:theory&application, 2020, 43(6):194-199, 193.)
[20] 张金柱,张晓林.利用引用科学知识突变识别突破性创新[J].情报学报, 2014, 33(3):259-266.(ZHANG J Z, ZHANG X L. Identification of radical innovation based on mutation of cited scientific knowledge[J]. Journal of the China Society for Scientific and Technical Information, 2014, 33(3):259-266.)
[21] DOTSIKA F, WATKINS A. Identifying potentially disruptive trends by means of keyword network analysis[J]. Technological forecasting&social change, 2017, 119:114-127.
[22] CHOI Y, PARK S, LEE S. Identifying emerging technologies to envision a future innovation ecosystem:a machine learning approach to patent data[J]. Scientometrics, 2021, 126(7):5431-5476.
[23] LIU X, WANG X, LV L, et al. Identifying disruptive technologies by integrating multi-source data[J]. Scientometrics, 2022, 127(9):5325-5351.
[24] CAPPONI G, MARTINELLI A, NUVOLARI A. Breakthrough innovations and where to find them[J]. Research policy, 2022, 51(1):104376.
[25] 梁镇涛,毛进,李纲.融合"科学-技术"知识关联的高颠覆性专利预测方法[J].情报学报, 2023, 42(6):649-662.(LIANG Z T, MAO J, LI G. Integrating science-technology knowledge linkage to predict disruptive patents[J]. Journal of the China Society for Scientific and Technical Information, 2023, 42(6):649-662.)
[26] VICKERS A J, ELKIN E B. Decision curve analysis:a novel method for evaluating prediction models[J]. Medical decision making, 2006, 26(6):565-574.
[27] 何燕,穗志方,段慧明,等.一种结合术语部件库的术语提取方法[J].计算机工程与应用, 2006, 42(33):4-7.(HE Y, SUI Z F, DUAN H M. Term mining combining term component bank[J]. Computer engineering and applications, 2006, 42(33):4-7.)
[28] FRANTZI K, ANANIADOU S, MIMA H. Automatic recognition of multi-word terms:. the C-value/NC-value method[J]. International journal on digital libraries, 2000, 3(2):115-130.
[29] MILIOS E, ZHANG Y, HE B, et al. Automatic term extraction and document similarity in special text corpora[C]//Proceedings of the sixth conference of the pacific association for computational linguistics. Halifax:Pacific Association for Computational Linguistics, 2003, 275-284.
[30] CHOUEKA Y, KLEIN S T, NEUWITZ E. Automatic retrieval of frequent idiomatic and collocational expressions in a large corpus[J]. Journal of the Association for Literary and Linguistic Computing, 1983, 4(1):34-38.
[31] CHURCH K, HANKS P. Word association norms, mutual information, and lexicography[J]. Computational linguistics, 1990, 16(1):22-29.
[32] FRANTZI K T. Incorporating context information for the extraction of terms[C]//Proceedings of the 35th annual meeting of the Association for computational linguistics and 8th conference of the European Chapter of the Association for Computational Linguistics. Madrid:Association for Computational Linguistics, 1997:501-503.
[33] DAMERAU F J. Generating and evaluating domain-oriented multi-word terms from texts[J]. Information processing&management, 1993, 29(4):433-447.
[34] COHEN J D. Highlights:language-and domain-independent automatic indexing terms for abstracting[J]. Journal of the American Society for Information Science, 1995, 46(3):162-174.
[35] 吴俊,程垚,郝瀚,等.基于BERT嵌入BiLSTM-CRF模型的中文专业术语抽取研究[J].情报学报, 2020, 39(4):409-418.(WU J, CHENG Y, HAO H, et al. Automatic extraction of Chinese terminology based on BERT embedding and BiLSTMCRF model[J]. Journal of the China Society for Scientific and Technical Information, 2020, 39(4):409-418.)
[36] DAILLE B. Study and implementation of combined techniques for automatic extraction of terminology[C]//The Balancing Act:combining symbolic and statistical approaches to language. Kennebunkport:Association for Computational Linguistics, 1994:29-36.
[37] FAHMI I, BOUMA G, VAN DER PLAS L. Improving statistical method using known terms for automatic term extraction[C]//Proceedings of Computational Linguistics in the Netherlands-CLIN 17. Netherlands:Computational Linguistics, 2007:9-16.
[38] 章成志.基于多层术语度的一体化术语抽取研究[J].情报学报, 2011, 28(3):275-285.(ZHANG C Z. Research on integrated term extraction based on multi-layer term degree[J]. Journal of the China Society for Scientific and Technical Information, 2011, 28(3):275-285.)
[39] 张勇.中文术语自动抽取相关方法研究[D].武汉:华中师范大学, 2006.(ZHANG Y. The study of automatic Chinese term extraction[D]. Wuhan:Central China Normal University, 2006.)
[40] XU S, LIU J, ZHAI D, et al. Overlapping thematic structures extraction with mixed-membership stochastic blockmodel[J]. Scientometrics, 117(1):61-84.
[41] STRUMSKY D, LOBO J. Identifying the sources of technological novelty in the process of invention[J]. Research policy, 2015, 44(8):1445-1461.
[42] VERHOEVEN D, BAKKER J, VEUGELERS R. Measuring technological novelty with patent-based indicators[J]. Research policy, 2016, 45(3):707-723.
[43] UZZI B, MUKHERJEE S, STRINGER M, et al. Atypical combinations and scientific impact[J]. Science, 2013, 342(6157):468-472.
[44] VEUGELERS R, WANG J. Scientific novelty and technological impact[J]. Research policy, 2019, 48(6):1362-1372.
[45] YAN Y, TIAN S, ZHANG L. The impact of a paper's new combinations and new components on its citation[J]. Scientometrics, 2020, 122(2):895-913.
[46] KE Q. Technological impact of biomedical research:the role of basicness and novelty[J]. Research policy, 2020, 49(7):104071.
[47] LIU M, BU Y, CHEN C, et al. Pandemics are catalysts of scientific novelty:evidence from COVID-19[J]. Journal of the Association for Information Science and Technology, 2022, 73(8):1065-1078.
[48] 安欣,徐硕,叶书路,等.面向全文本的微观实体抽取及扩散研究[J].图书馆论坛, 2021, 41(3):42-49.(AN X, XU S, YE S L, et al. Research on extraction and diffusion of micro-level entities based on full texts[J]. Library tribune, 2021, 41(3):42-49.)
[49] CHEN L, XU S, ZHU L, et al. A deep learning based method for extracting semantic information from patent documents[J]. Scientometrics, 2020, 125(1):289-312.
[50] XU S, AN X, ZHU L, et al. A CRF-based system for recognizing chemical entity mentions (CEMs) in biomedical literature[J]. Journal of cheminformatics, 2015, 7(S1):S11.
[51] FUNK R J, OWEN-SMITH J. A dynamic network measure of technological change[J]. Management science, 2017, 63(3):791-817.
[52] WU L, WANG D, EVANS J A. Large teams develop and small teams disrupt science and technology[J]. Nature, 2019, 566(7744):378-382.
[53] JAFFE A B, TRAJTENBERG M. Patents, citations, and innovations:a window on the knowledge economy[M]. Cambridge:MIT Press, 2022.
[54] YANG G, LU G, XU S, et al. Which type of dynamic indicators should be preferred to predict patent commercial potential[J]. Technological forecasting&social change, 2023, 193:122637.
[55] MANNING C D, SURDEANU M, BAUER J, et al. The Stanford CoreNLP natural language processing toolkit[C]//Proceedings of the 52nd annual meeting of the Association for Computational Linguistics. Baltimore:Association for Computational Linguistics, 2014:55-60.
[56] DAGAN I, CHURCH K. Termight:identifying and translating technical terminology[C]//Proceedings of the 4th conference on applied natural language processing. Stuttgart:Association for Computational Linguistics, 1994:34-40.
[57] NEMET G F, HUSMANN D. PV learning curves and cost dynamics[J]. Semiconductors and semimetals, 2012, 87:85-142.
[58] CHEN T, GUESTRIN C. XGBoost:a scalable tree boosting system[C]//Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. San Francisco:Computer Science, 2016:785-794.