基于主题N元语法模型的科技报告主题分析

doi:10.13998/j.cnki.issn1002-1248.2019.06.19-0550

农业图书情报 ›› 2019, Vol. 31 ›› Issue (6): 21-30.doi: 10.13998/j.cnki.issn1002-1248.2019.06.19-0550

基于主题N元语法模型的科技报告主题分析

安欣¹, 徐硕²

1. 北京林业大学经济与管理学院,北京 100083;
2. 北京工业大学经济与管理学院北京现代制造业发展研究基地,北京 100124

收稿日期:2019-06-24 出版日期:2019-06-05 发布日期:2019-08-02
通讯作者: 徐硕（1979-）,男,ORCID：0000-0002-8602-1819,博士,教授,北京工业大学,研究方向：科学前沿探测、技术预见、大数据和数据挖掘等,E-mail: xushuo@bjut.edu.cn。
作者简介:安欣（1980-）,女,博士,副教授,北京林业大学,研究方向：科学计量、知识发现、知识组织与管理等,E-mail: anxin@bjfu.edu.cn。
基金资助:
广东省自然科学基金项目“面向生物医药领域的前沿技术预判方法论与模型构建研究”（项目编号：2018A030313695）

Topical Analysis of Scientific and Technical Reports based on Topical N-Grams Model

AN Xin¹, XU Shuo²

1. School of Economics and Management, Beijing Forestry University, Beijing 100083, China;
2. Research Base of Beijing Modern Manufacturing Development, College of Economics and Management, Beijing University of Technology, Beijing 100124, Chin

Received:2019-06-24 Online:2019-06-05 Published:2019-08-02

摘要/Abstract

摘要： 作为科技情报的重要载体之一,科技报告可以反映科技发展的脉络,可以揭示科技前沿的动态,甚至可以洞察科技发展的趋势等。中国科技报告的开发利用研究目前主要集中在书本型科技报告或电子出版物的出版发行、数据库建设、服务方式和知识产权等方面,在深度数据挖掘方面的研究工作相对较少。笔者尝试利用主题N元语法模型对科技报告进行领域深层主题分析,为了确定特定领域科技报告的主题数目,笔者借助动态规划的思想针对主题N元语法模型提出了困惑度的有效计算方法。最后,以肿瘤领域

关键词: 科技报告, 主题N元语法模型, 主题分析, 困惑度, 热力图

Abstract: As one of the important carriers of scientific & technical (S&T) intelligence, S&T reports can reflect the line of S&T development, recover the latest news of S&T fronts, and even insight the trends of S&T development. Researches on developing and utilizing S&T reports in our country mainly focus on the following: publication and distribution of S&T reports in the form of book and electrical publication; database construction; service mode; intelligent property and so on. The deep data mining on S&T reports remains largely under-studied. This work tries to discover the domain latent topics of S&T reports with the topical n-grams model. In order to determine the number of topics of S&T reports for some specific domain, the calculation method of perplexity of the topic n-grams model is put forward with the dynamic programming in this study. Finally, 70 domain topics are discovered from 1 344 S&T reports in the tumor domain, such as "molecular mechanisms/tumor cells", "system biology/key methods" and so on. Experimental results show that it is feasible and efficient to discover the latent topics from S&T reports with the topical n-grams model.

Key words: scientific and technical reports, topical n-grams model, topical analysis, perplexity, heat map

中图分类号:

G322

安欣, 徐硕. 基于主题N元语法模型的科技报告主题分析[J]. 农业图书情报, 2019, 31(6): 21-30.

AN Xin, XU Shuo. Topical Analysis of Scientific and Technical Reports based on Topical N-Grams Model[J]. Agricultural Library and Information, 2019, 31(6): 21-30.

参考文献

[1] 贺德方. 中国科技报告制度的建设方略[J]. 情报学报, 2013, 32(5): 452-458.
[2] 张爱霞, 沈玉兰. 美国政府科技报告体系建设现状分析[J]. 情报学报, 2007, 26(4), 496-502.
[3] 赵俊杰. 美国科技报告体系建设概况[J]. 全球科技经济瞭望, 2013, 28(3): 1-7.
[4] 张爱霞. 美国能源部科技报告管理和服务现状分析[J]. 图书情报工作, 2007, 51(1): 89-92.
[5] 吴蓉, 顾立平, 曾燕. 英国科技报告制度调研与分析——支持科技报告存储与传播的政策环境[J]. 图书情报工作, 2015, 59(21):76-82.
[6] 贺德方, 胡红亮, 周杰. 中国科技报告体系的建设模式研究[J]. 情报学报, 2009, 28(6): 804-808.
[7] 张铣清. 对发展中国科技报告工作的探讨[J]. 中国科技论坛, 1995, (6): 35-38.
[8] 胡红亮. 建立中国科技报告体系势在必行[J]. 全球科技经济瞭望, 2007, (2): 33-35.
[9] 楚明超. 美国NTIS介绍及对我国科技报告制度建设的启发[J]. 科技成果管理与研究, 2013, (8): 32-34.
[10] 周杰. 科技报告资源的构成及产生机理研究[J]. 情报学报, 2013, 32(5): 466-471.
[11] 张新民. 我国科技报告制度体系框架设计研究与实施进展[J]. 中国科技资源导刊, 2013, (3): 1-6, 40:
[12] 曾建勋. 科技报告技术标准体系研究[J]. 情报学报, 2013, 32(5): 459-465.
[13] 侯人华. 科技报告政策体系及服务方式研究[J]. 情报学报, 2013, 32(5): 472-477.
[14] 陈传夫, 代钰珠, 曾建勋. 科技报告开发利用与知识产权问题研究[J]. 情报学报, 33(8): 793-799.
[15] 贺德方. 科技报告资源体系研究[J]. 信息资源管理学报, 2013, (1): 4-9, 31.
[16] 夏立新, 李成龙. 基于关联数据的科技报告语义共享框架设计与实现[J]. 数字图书馆论坛, 2015, (9): 1-9.
[17] 石军. 互联网科技报告的检索和开发利用[J]. 内蒙古科技与经济, 2002, 1: 56-57.
[18] Blei D, Ng A and Jordan M. Latent Dirichlet Allocation[J]. Journal of Machine Learning Re-search, 2003, 3: 993-1022.
[19] Griffths T and Steyvers M. Finding scientific topics[C]. // Proceedings of the National Academy of Sciences of the United States of America, 2004, 101(Suppl. 1):5228-5235.
[20] Xu S, Zhu L, Qiao X et al. Topic Linkages between Papers and Patents[C] // Proceedings of the 4th AST International Conference on Advanced Science and Technology, Daejeon: SERSC press, 2012:176-183.
[21] 史庆伟, 乔晓东, 徐硕等. 作者主题演化模型及其在研究兴趣演化分析中的应用[J]. 情报学报, 2013, 32(9): 912-919.
[22] Xu S, Shi Q, Qiao X, et al.Author-Topic over Time (AToT): A Dynamic Users’ Interest Model[C]. // Proceedings of the 5th International Conference on Mobile, Ubiquitous, and Intelligent Computing, 2014: 239-245.
[23] Xu S, Shi Q, Qiao X, et al.A Dynamic Users’ Interest Discovery Model with Distributed Inference Algorithm[J]. International Journal of Distributed Sensor Networks, 2014, 280892: 1-11.
[24] An X, Xu S, Wen Y, et al.A Shared Interest Discovery Model for Coauthor Relationship in SNS[J]. International Journal of Distributed Sensor Networks, 2014, 2014(820715): 1-9.
[25] Xu S, Liu J, Zhai D, et al.Overlapping Thematic Structures Extraction with Mixed-Membership Stochastic Blockmodel[J]. Scientometrics, 2018, 117(1): 61-84.
[26] Xu S, Zhai D, Wang F, et al.A Novel Method for Topic Linkages between Scientific Publications and Patents[J]. Journal of the Association for Information Science and Technology. DOI: 10.1002/asi.24175
[27] Zhang H, Xu S, Qiao X, et al.Infinite Coauthor Topic Model (Infinite coAT): A Non-Parametric Generalization for coAT Model[C]. // Proceedings of the 1st International Workshop on Patent Mining and its Applications (IPaMin), 2014.
[28] Tang J, Wang B, Yang Y, et al.PatentMiner: Topic-Driven Patent Analysis and Mining[C] // Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, NY: ACM, 2012: 1366-1374.
[29] Newman D, Chemudugunta C,Smyth P.Statistical Entity-Topic Models[C] // Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, NY: ACM, 2006: 680-686.
[30] Ni X, Sun J-T, Hu J, et al.Mining Multilingual Topics from Wikipedia[C] // Proceedings of the 18th International Conference on World Wide Web, NY: ACM, 2009: 1155-1156.
[31] Tsai F S.A Tag-Topic Model for Blog Mining[J]. Expert System with Applications, 38(5): 5330-5335.
[32] Blei D M.Introduction to Probabilistic Topic Models[J]. Communications of the ACM, 2012, 55(4): 77-84.
[33] 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.
[34] 徐戈, 王厚峰. 自然语言处理中主题模型的发展[J]. 计算机学报, 2011, 34(8): 1423-1436.
[35] Daud A, Li J, Zhou L, et al.Knowledge Discovery through Directed Probabilistic Topic Models: A Survey[J]. Frontiers of Computer Science in China, 2010, 4(2): 280-301.
[36] Griffiths T L, Steyvers M, Blei D M, et al.Integrating Topics and Syntax [C] // Advances in Neural Information Processing System 17, MA: MIT Press, 2005: 537-544.
[37] Rabiner L R.A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition[J]. Proceedings of the IEEE, 1989, 77(2): 257-286.
[38] 徐硕. 基于机器学习方法的硒蛋白基因预测[D]. 北京:中国农业大学, 2008.
[39] Wallach H M.Topic Modeling: Beyond Bag-of-Words [C]. // Proceedings of the 23rd International Conference on Machine Learning, New York: ACM Press, 2006: 977-984.
[40] Griffiths T L, Steyvers M,Tenenbaum J B.Topics in Semantic Representation[J]. Psychological Review, 2007, 114(2): 211-244.
[41] Wang X, McCallum A and Wei X. Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval [C]. // Proceedings of the 7th International Conference on Data Mining, New York: ACM Press, 2007: 697-702.
[42] Wang Z, Xu S,Zhu L.Semantic Relation Extraction Aware of N-Gram Features from Unstructured Biomedical Text[J]. Journal of Biomedical Informatics, 2018, 86: 59-70.
[43] Mann G S, Mimno D,McCallum A. Bibliometric Impact Measures Leveraging Topic Analysis [C]. // Proceedings of the 6th ACM/IEEE-CS Joint Conference on Digital Libraries, 2006: 65-74. [44] Teh Y, Newman D and Welling M. A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation[J]. Advances in neural information processing systems 2007, 19: 1353-1360.
[45] Azzopardi L, Girolami M, van Risjbergen K, et al. Investigating the relationship between language model perplexity and IR precision-recall measures[C]. // Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, New

基于主题N元语法模型的科技报告主题分析

Topical Analysis of Scientific and Technical Reports based on Topical N-Grams Model

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 2

Metrics

本文评价

推荐阅读 0

[1]	赵磊, 章成志. 基于不同内容层面的特定领域研究主题差异分析研究[J]. 农业图书情报学报, 2021, 33(5): 14-27.
[2]	葛艳聪. 2008年—2012年国内图书馆法研究述评[J]. 农业图书情报学刊, 2014, 26(8): 57-60.