Please wait a minute...
New Technology of Library and Information Service  2016, Vol. 32 Issue (10): 13-24    DOI: 10.11925/infotech.1003-3513.2016.10.02
Orginal Article Current Issue | Archive | Adv Search |
Semantic Text Mining Methodologies for Intelligence Analysis
Zhao Dongxiao(),Wang Xiaoyue,Bai Rujiang,Liu Ziqiang
Institute of Scientific & Technical Information, Shandong University of Technology, Zibo 255049, China
Download: PDF(601 KB)   HTML ( 41
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This paper reviews the semantic text mining techniques for intelligence analysis. [Coverage] We surveyed the leading semantic text mining research on intelligence analysis from the last ten years and a few earlier studies. [Methods] We first discussed the semantic text mining methodologies and algorithms for words, sentences and paragraphs. Then, we analyzed these techniques from the perspective of topic evolution and applications of mining technologies. [Results] Compared to the traditional intelligence analysis methods, semantic text mining approaches could process unstructured data and deal with multi-layer structured data. [Limitations] Only reviewed the leading studies and their applications in the scientific field. [Conclusions] Semantic text mining improve the performance of traditional intelligence analysis systems and become the future direction of research methodology. More research is needed to enrich the outlier semantic resources.

Key wordsSemantic text mining      Intelligence analysis      Topic evolution      Technology mining     
Received: 06 June 2016      Published: 23 November 2016

Cite this article:

Zhao Dongxiao,Wang Xiaoyue,Bai Rujiang,Liu Ziqiang. Semantic Text Mining Methodologies for Intelligence Analysis. New Technology of Library and Information Service, 2016, 32(10): 13-24.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2016.10.02     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2016/V32/I10/13

[1] Kantardzic M.数据挖掘: 概念、模型、方法和算法[M]. 王晓海, 吴志刚译. 北京: 清华大学出版社, 2013: 250-251.
[1] (Kantardzic M.Data Mining: Concepts, Models, Methods, and Algorithms [M]. Translated by Wang Xiaohai, Wu Zhigang. Beijing: Tsinghua University Press, 2013: 250-251.)
[2] 王丽杰, 车万翔, 刘挺. 基于SVMTool的中文词性标注[J]. 中文信息学报, 2009, 23(4): 16-21.
[2] (Wang Lijie, Che Wanxiang, Liu Ting.An SVMTool-Based Chinese POS Tagger[J]. Journal of Chinese Information Processing, 2009, 23(4): 16-21.)
[3] 张民, 李生, 赵铁军, 等. 统计与规则并举的汉语词性自动标注算法[J]. 软件学报, 1998, 9(2): 134-138.
[3] (Zhang Min, Li Sheng, Zhao Tiejun, et al.Part of Speech Tagging Chinese Corpus Based on Statistics and Rules[J]. Journal of Software, 1998, 9(2): 134-138.)
[4] 郭永辉, 吴保民, 王炳锡. 一种用于词性标注的相关投票融合策略[J]. 中文信息学报, 2007, 21(2): 9-13.
[4] (Guo Yonghui, Wu Baomin, Wang Bingxi.Correlation Voting Fusion Strategy Used for Part of Speech Tagging[J]. Journal of Chinese Information Processing, 2007, 21(2): 9-13.)
[5] 洪铭材, 张阔, 唐杰, 等. 基于条件随机场CRFs的中文词性标注方法[J]. 计算机科学, 2006, 33(10): 148-155.
[5] (Hong Mingcai, Zhang Kuo, Tang Jie, et al.A Chinese Part-of- Speech Tagging Approach Using Conditional Random Fields[J]. Computer Science, 2006, 33(10): 148-155.)
[6] 张民, 李生, 赵铁军, 等. 统计与规则并举的汉语词性自动标注算法[J]. 软件学报, 1998, 9(2): 134-138.
[6] (Zhang Min, Li Sheng, Zhao Tiejun, et al.Part of Speech Tagging Chinese Corpus Based on Statistics and Rules[J]. Journal of Software, 1998, 9(2): 134-138.)
[7] ICTCLAS[K]. [2015-07-28]. CTCLAS[K]. [2015-07-28]. .
[8] 哈工大语言云[K]. [2015-08-13]. 工大语言云[K]. [2015-08-13]. .
[8] (LTP[K]. [2015-08-13]. TP[K]. [2015-08-13].
[9] Stanford Log-linear Part-Of-SpeechTagger[K]. [2015-09-15]. tanford Log-linear Part-Of-SpeechTagger[K]. [2015-09-15]. .
[10] CLAWS POS Tagger[K]. [2015-09-18]. LAWS POS Tagger[K]. [2015-09-18]. .
[11] NLTK [K]. [2015-07-20]. LTK [K]. [2015-07-20]. .
[12] 商宪丽, 王学东.微博话题识别中基于动态共词网络的文本特征提取方法[J]. 图书情报知识, 2016(3): 80-88.
[12] (Shang Xianli, Wang Xuedong.A Feature Selection Method Based on Dynamic Co-word Network for Microblog Topic Detection[J]. Documentation, Information&Knowledge, 2016(3): 80-88.)
[13] 杜思奇, 李红莲, 吕学强. 基于汉语组块分析的情感标签抽取[J]. 情报理论与实践, 2016, 39(5): 125-129.
[13] (Du Siqi, Li Honglian, Lv Xueqiang.Chinese Chunking Based Emotional Label Extraction[J]. Information Studies: Theory & Application, 2016, 39(5): 125-129.)
[14] 兰秋军, 刘文星, 李卫康, 等. 融合句法信息的金融论坛文本情感计算研究[J]. 现代图书情报技术, 2016(4): 64-71.
[14] (Lan Qiujun, Liu Wenxing, Li Weikang, et al.Sentiment Analysis of Financial Forum Textual Message[J]. New Technology of Library and Information Service, 2016(4): 64-71.)
[15] 翟羽佳, 王芳. 基于文本挖掘的中文领域本体构建方法研究[J]. 情报科学, 2015, 33(6): 3-10.
[15] (Zhai Yujia, Wang Fang.Research on Construction Methods of Chinese Domain Ontology Based on Text Mining[J]. Information Science, 2015, 33(6): 3-10.)
[16] 吴云芳. 词义消歧研究: 资源、方法与评测[J]. 当代语言学, 2009, 11(2): 113-123.
[16] (Wu Yunfang.A Survey of Chinese Word Sensedisambiguation: Resources, Methods and Evaluation[J]. Contemporary Linguistics, 2009, 11(2): 113-123.)
[17] 卢志茂, 刘挺, 李生. 统计词义消歧的研究进展[J]. 电子学报, 2006, 34(2): 333-343.
[17] (Lu Zhimao, Liu Ting, Li Sheng.The Research Progress of Statistical Word Sense Disambugation[J]. Electronic Sinica, 2006, 34(2): 333-343.)
[18] Lesk M E.Automated Sense Disambiguation Using Machine Readable Dictionaries: How to Tell a Pine Cone from All Ice Cream Cone[C]. In: Proceedings of the S1GDOC Conference. New York: Association for Computing Machinery, 1986: 24-26.
[19] Pook S L, Catlett J.Making Sense out of Searching[R]. Sydney: AT&T Bell Laboratories, 1988.
[20] Agirre E, Rigau G.A Proposal for Word Sense Disambiguation Using Conceptual Distance [C]. In: Proceedings of the 1st International Conference on Recent Advances in NLP. 1995: 162-171.
[21] 鹿文鹏, 黄河燕, 吴昊. 基于领域知识的图模型词义消歧方法[J]. 自动化学报, 2014, 40(12): 2836-2850.
[21] (Lu Wenpeng, Huang Heyan, Wu Hao.Word Sense Disambiguation Based with Graph Model Based on Domain Knowledge[J]. Acta Automatic Sinica, 2014, 40(12): 2836-2850.)
[22] 张仰森, 郭江. 四种统计词义消歧模型的分析与比较[J]. 北京信息科技大学学报, 2011, 26(2): 13-18.
[22] (Zhang Yangsen, Guo Jiang.Analysis and Comparison of 4 Kinds of Statistical Word Sense Disambiguation Models[J]. Journal of Beijing Information Science & Technology, 2011, 26(2): 13-18.)
[23] 鲁松, 白硕, 黄雄, 等. 基于向量空间模型的有导消歧[J]. 计算机研究与发展, 2011, 38(6): 662-667.
[23] (Lu Song, Bai Shuo, Huang Xiong, et al.Supervised Word Sense Disambiguation Based on Vector Space Model[J]. Computer Research and Development, 2011, 38(6): 662-667.)
[24] 王瑞琴, 孔繁胜. 无监督词义消歧研究[J]. 软件学报, 2009, 20(8): 2138-2152.
[24] (Wang Ruiqin, Kong Fansheng.Unsupervised Word Sense Disambiguation Research[J]. Journal of Software, 2009, 20(8): 2138-2152.)
[25] BRAT [K]. [2015-09-18]. RAT [K]. [2015-09-18]. .
[26] 杨建林, 王文龙. 公共卫生类突发事件的抽取研究[J]. 情报理论与实践, 2016, 39(4): 51-59.
[26] (Yang Jianlin, Wang Wenlong.Public Sanitation Emergency Event Extraction[J]. Information Studies: Theory & Application, 2016, 39(4): 51-59.)
[27] 陈锋, 翟羽佳, 王芳. 基于条件随机场的学术期刊中理论的自动识别方法[J]. 图书情报工作, 2016, 60(2): 122-128.
[27] (Chen Feng, Zhai Yujia, Wang Fang.Automatic Theory Recognition in Academic Journals Based on CRF[J]. Library and Information Service, 2016, 60(2): 122-128.)
[28] 祝娜, 王效岳, 白如江. 语义角色标注及其在科技情报分析中的应用研究[J]. 情报理论与实践, 2015, 38(1): 98-103.
[28] (Zhu Na, Wang Xiaoyue, Bai Rujiang.Semantic Role Labeling and the Application in Intelligence Analysis[J]. Information Studies: Theory & Application, 2015, 38(1): 98-103.)
[29] Hacioglu K.Semantic Role Labeling Using Dependency Trees [C]. In: Proceedings of the 20th International Conference on Computational Linguistics. Association for Computational Linguistics, 2004.
[30] 王步康, 王红玲, 袁晓虹, 等. 基于依存句法分析的中文语义角色标注[J]. 中文信息学报, 2010, 24(1): 25-29.
[30] (Wang Bukang, Wang Hongling, Yuan Xiaohong, et al.Chinese Dependency Parse Based Semantic Role Labeling[J]. Journal of Chinese Information Processing, 2010, 24(1): 25-29.)
[31] Gildea D, Palmer M.The Necessity of Parsing for Predicate Argument Recognition [C]. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. 2002: 239-246.
[32] Pradhan S, Ward W, Hacioglu K, et al.Shallow Semantic Parsing Using Support Vector Machines [C]. In: Proceedings of HLT-NAACL.2004: 233-240.
[33] 李世奇, 赵铁军, 李晗静, 等. 基于特征组合的中文语义角色标注[J]. 软件学报, 2011, 22(2): 222-232.
[33] (Li Shiqi, Zhao Tiejun, Li Hanjing, et al.Chinese Semantic Role Labeling Based on Feature Combination[J]. Journal of Software, 2011, 22(2): 222-232.)
[34] 王红玲. 基于特征向量的中英文语义角色标注研究[D]. 苏州: 苏州大学, 2009.
[34] (Wang Hongling.Chinese and English Semantic Role Labeling Based on Feature Vector [D]. Suzhou: Soochow University, 2009.)
[35] 宋毅君, 王瑞波, 李济洪, 等. 基于条件随机场的汉语框架语义角色自动标注[J]. 中文信息学报, 2014, 28(3): 36-47.
[35] (Song Yijun, Wang Ruibo, Li Jihong.et al.Semantic Role Labeling of Chinese FrameNet Based on Conditional Random Fields[J]. Journal of Chinese Information Processing, 2014, 28(3): 36-47.)
[36] 李明, 王亚斌, 张其文, 等. 基于树状条件随机场模型的语义角色标注[J]. 计算机工程, 2010, 36(18): 41-45.
[36] (Li Ming, Wang Yabin, Zhang Qiwen, et al.Semantic Role Labeling Based on Tree Conditional Random Fields Model[J]. Computer Engineering, 2010, 36(18): 41-45.)
[37] 白如江, 祝娜, 王效岳. 语义增强的科技创新内容表征研究[J]. 情报理论与实践, 2016, 39(3): 73-79.
[37] (Bai Rujiang, Zhu Na, Wang Xiaoyue.Semantic Representation of Technical Innovation Content Based on Semantic Enhancement[J]. Information Studies: Theory & Application, 2016, 39(3): 73-79.)
[38] 张帆, 乐小虬. 领域科技文献创新点句中主题属性实例识别方法研究[J]. 现代图书情报技术, 2015(5): 15-23.
[38] (Zhang Fan, Le Xiaoqiu.Research on Recognition of Concept Attribute Instances in Innovation Sentences of Scientific Research Paper[J]. New Technology of Library and Information Service, 2015 (5): 15-23.)
[39] 祝娜, 王效岳, 杨京, 等. 基于LDA 的科技创新主题语义识别研究[J]. 图书情报工作, 2015, 59(14): 126-134.
[39] (Zhu Na, Wang Xiaoyue, Yang Jing, et al.Semantic Recognition of Technological Innovation Theme Based on LDA[J]. Library and Information Service, 2015, 59(14): 126-134.)
[40] 洪韵佳, 许鑫. 基于领域本体的知识库多层次文本聚类研究——以中华烹饪文化知识库为例[J]. 现代图书情报技术, 2013(12): 19-26.
[40] (Hong Yunjia, Xu Xin.Study on Multi-Level Text Clustering for Knowledge Base Based on Domain Ontology——Taking Knowledge Base of Chinese Cuisine Culture as an Example[J]. New Technology of Library and Information Service, 2013(12): 19-26.)
[41] 常娥. 基于LSI 理论的文本自动聚类研究[J]. 图书情报工作, 2012, 56(11): 89-92.
[41] (Chang E.Automatic Text Clustering Based on Latent Semantic Index Theory[J]. Library and Information Service, 2012, 56(11): 89-92.)
[42] 叶春蕾, 冷伏海. 基于共词分析的学科主题演化方法改进研究[J]. 情报理论与实践, 2012, 35(3): 79-82.
[42] (Ye Chunlei, Leng Fuhai.Development of Discipline Theme Evolution Analysis Based on Co-word Analysis[J]. Information Studies: Theory & Application, 2012, 35(3): 79-82.)
[43] 唐晓波, 房小可. 基于文本聚类与 LDA 相融合的微博主题检索模型研究[J]. 情报理论与实践, 2013, 36(8): 85-90.
[43] (Tang Xiaobo, Fang Xiaoke.Micro Blog Topic Retrieval Model Research Based on Text Clustering and LDA[J]. Information Studies: Theory & Application, 2013, 36(8): 85-90.)
[44] Mitchell T.Machine Learning[M]. McCraw Hill, 1996.
[45] Yang Y.An Evaluation of Statistical Approaches to Text Categorization[J]. Information Retrieval, 1999, 1(1-2): 69-90.
[46] Church K W, Hanks P. Word Association Norms, Mutual Information and Lexicography[J]. Computational Linguistics, 1990, 16(1): 22-29.
[47] Google新闻的工作原理[EB/OL]. [2016-04-28]. Hl=zh-Hans&topic =2428790.
[47] (The Working Principle of Google News [EB/OL]. [2016-04-28]. Hl=zh-Hans&topic =2428790
[48] 新华网[EB/OL]. [2016-04-28]. .
[48] (xinhuanet [EB/OL]. [2016-04-28].
[49] 宁海燕. 实体关系自动抽取技术的比较研究[D]. 哈尔滨: 哈尔滨工业大学, 2010.
[49] (Ning Haiyan.Comparative Study of Automatic Entity Relation Extraction [D]. Harbin: Harbin Insititute of Technology, 2010.)
[50] 杨锦锋, 于秋滨, 关毅, 等. 电子病历命名实体识别和实体关系抽取研究综述[J]. 自动化学报, 2014, 40(8): 1537-1560.
[50] (Yang Jinfeng, Yu Qiubin, Guan Yi, et al.An Overview of Research on Electronic Medical Record Oriented Named Entity Recognition and Entity Relation Extraction[J]. Acta Automatic Sinica, 2014, 40(8): 1537-1560.)
[51] 候跃芳, 崔雷, 吴迪. 应用引文共引聚类-内容词分析法对学科发展的研究[J]. 情报学报, 2007, 26(2): 309-314.
[51] (Hou Yuefang, Cui Lei, Wu Di.Co-Citation Clustering-Content Words Analysis in Subject Development[J]. Journal of the China Society for Scientific and Technical Information, 2007, 26(2): 309-314.)
[52] 柴省三. 内容词-共引聚类分析及其在科学结构研究中的应用[J]. 情报学报, 1997, 16(1): 68-73.
[52] (Chai Shengsan.Application of Content Words and Co-citation Clustering Analysis to Science Structure Studies[J]. Journal of the China Society for Scientific and Technical Information, 1997, 16(1): 68-73.)
[53] Callon M, Law J, Rip A.Mapping the Dynamics of Science and Technology: Sociology of Science in the Real World[M]. London: The Macmillan Press LTD, 1998.
[54] 崔雷. 当年高被引论文的主题词链聚类分析及其在情报预测中的应用[J]. 情报学报, 1995, 14(5): 368-373.
[54] (Cui Lei.Keyword Link Cluster Analysis of the Immediately Highly Cited Papers and Its Utilization in Information Prediction[J]. Journal of the China Society for Scientific and Technical Information, 1995, 14(5): 368-373.)
[55] Callon M, Courtial J P, Laville F.Co-word Analysis as a Tool for Describing the Network of Interactions Between Basic and Technological Research: The Case of Polymer Chemistry[J]. Scientometrics, 1991, 22(1): 155-205.
[56] Kostoff R N, Eberhart H J, Toothman D R.Data-base Tomography for Technical Intelligence: A Roadmap of The Near-earth Space Science and Technology Literature[J]. Information Processing & Management, 1997, 34(1): 69-85.
[57] 王晓光. 科学知识网络的结构与演化(Ι): 共词网络方法的提出[J]. 情报学报, 2009, 28(4): 599-605.
[57] (Wang Xiaoguang.Structure and Evolution of Scientific Knowledge Network: Co-word Network[J]. Journal of the China Society for Scientific and Technical Information, 2009, 28(4): 599-605.)
[58] 白如江, 冷伏海. k-clique社区知识创新演化方法研究[J]. 图书情报工作, 2013, 57(17): 86-94.
[58] (Bai Rujiang, Leng Fuhai.Knowledge Innovational Evolution Analysis Based on k-clique Community Network[J]. Library and Information Service, 2013, 57(17): 86-94.)
[59] 郑彦宁, 许晓阳, 刘志辉. 基于关键词共现的研究前沿识别方法研究[J]. 图书情报工作, 2016, 60(4): 85-92.
[59] (Zheng Yanning, Xu Xiaoyang, Liu Zhihui.Study on the Method of Identifying Research Fronts Based on Keywords Co-occurrence[J]. Library and Information Service, 2016, 60(4): 85-92.)
[60] 巴志超, 杨子江, 朱世伟, 等. 基于关键词语义网络的领域主题演化分析方法研究[J]. 情报理论与实践, 2016, 39(3): 67-72.
[60] (Ba Zhichao, Yang Zijiang, Zhu Shiwei, et al.Key Words Semantic Network Based Field Topic Evolution Analysis Model[J]. Information Studies: Theory & Application, 2016, 39(3): 67-72.)
[61] 陈千, 桂志国, 郭鑫, 等. 基于特征本体的文本流主题演化[J]. 计算机应用, 2015, 35(2): 456-460.
[61] (Chen Qian, Gui Zhiguo, Guo Xin, et al.Topic Evolution in Text Stream Based on Feature Ontology[J]. Journal of Computer Applications, 2015, 35(2): 456-460.)
[62] 王平. 基于层次概率主题模型的科技文献主题发现及演化[J]. 图书情报工作, 2014, 58(22): 70-77.
[62] (Wang Ping.Topic Extraction and Evolution for Scientific Literature Based on Hierarchical Probabilistic Topic Model[J]. Library and Information Service, 2014, 58(22): 70-77.)
[63] 何建民, 李雪. 面向微博舆情演化分析的隐马尔科夫模型研究[J]. 情报科学, 2016, 34(4): 7-12.
[63] (He Jianmin, Li Xue.A Hidden Markov Model Research in the Microblog Public Opinion Evolutionary Analysis[J]. Information Science, 2016, 34(4): 7-12.)
[64] Song M, Heo G E, Kim S Y.Analyzing Topic Evolution in Bioinformatics: Investigation of Dynamics of the Field with Conference Data in DBLP[J]. Scientometrics, 2014, 101(1): 397-428.
[65] 胡正银, 方曙. 专利文本技术挖掘研究进展综述[J]. 现代图书情报技术, 2014(6): 62-70.
[65] (Hu Zhengyin, Fang Shu.Review of Patent Text Technology Mining Research Development[J]. New Technology of Library and Information Service, 2014(6): 62-70.)
[66] Yoon J, Kim K.Identifying Rapidly Evolving Technological Trends for R&D Planning Using SAO-based Semantic Patent Networks[J]. Scientometrics, 2011, 88(1): 213-228.
[67] Park H, Yoon J, Kim K.Using Function-based Patent Analysis to Identify Potential Application Areas of Technology for Technology Transfer[J]. Expert Systems with Applications, 2013, 40(13): 5260-5265.
[68] Yoon J, Kim K.Detecting Signals of New Technological Opportunities Using Semantic Patent Analysis and Outlier Detection[J]. Scientometrics, 2012, 90(2): 1-17.
[69] 胡正银, 方曙, 隗玲. 基于SAO的专利技术演化分析[C]. 见: 中国图书馆学会专业图书馆分会2015年年会论文集, 贵阳. 2015.
[69] (Hu Zhengyin, Fang Shu, Kui Ling.Patent Technology Evolution Analysis Based on SAO [C]. In: Proceedings of Professional Library Branch of China Library Association 2015 Scholar Conference, Guiyang. 2015.)
[1] Peiyao Zhang,Dongsu Liu. Topic Evolutionary Analysis of Short Text Based on Word Vector and BTM[J]. 数据分析与知识发现, 2019, 3(3): 95-101.
[2] Hongqinling Wang,Zhichao Ba,Gang Li. Conversational Topic Intensity Calculation and Evolution Analysis of WeChat Group[J]. 数据分析与知识发现, 2019, 3(2): 33-42.
[3] Yuemei Xu,Sining Lv,Lianqiao Cai,Xiaoya Zhang. Analyzing News Topic Evolution with Convolutional Neural Networks and Topic2Vec[J]. 数据分析与知识发现, 2018, 2(9): 31-41.
[4] Jingqi Wang,Rui Li,Huayi Wu. The Evolution of Online Public Opinion Based on Spatial Autocorrelation[J]. 数据分析与知识发现, 2018, 2(2): 64-73.
[5] Weilin He,Guohe Feng,Hongling Xie. Analyzing Scientific Literature with Content Similarity - Topics over Time Model[J]. 数据分析与知识发现, 2018, 2(11): 64-72.
[6] Wang Yuefen,Jin Jialin. Characteristics and Development Trends of Papers from “New Technology of Library and Information Service”[J]. 现代图书情报技术, 2016, 32(9): 1-16.
[7] Xu Yuemei,Li Yang,Liang Ye,Cai Lianqiao. Analyzing Evolution of News Topics with Manifold Learning[J]. 现代图书情报技术, 2016, 32(10): 59-69.
[8] Qin Xiaohui, Le Xiaoqiu. Topic Evolution Research on a Certain Field Based on LDA Topic Association Filter[J]. 现代图书情报技术, 2015, 31(3): 18-25.
[9] Hu Zhengyin, Fang Shu. Review on Text-based Patent Technology Mining[J]. 现代图书情报技术, 2014, 30(6): 62-70.
[10] Qian Li, Zhang Xiaolin, Li Chunwang, Wang Xiaomei, Yang Liying, Chen Ting, Zhang Zhixiong. Research and Application of Science Intelligence Analysis Integrated Services Architecture Using OSGi[J]. 现代图书情报技术, 2014, 30(12): 62-70.
[11] Zhao Yingguang, Hong Na, An Xinying. A Survey of the Approach of Topic Evolution Model Based on Topic Model[J]. 现代图书情报技术, 2014, 30(10): 63-69.
[12] He Liang, Li Fang. Topic Evolution in Scientific Literature[J]. 现代图书情报技术, 2012, 28(4): 61-67.
[13] Shan Bin, Li Fang. Topic Evolution Based on Seminal Document and Topic Model[J]. 现代图书情报技术, 2011, 27(7/8): 104-109.
[14] Wang Xiaomei,Wu Qingqiang,Han Tao. Practice of Integration for the Intelligent Analysis Platform[J]. 现代图书情报技术, 2007, 2(7): 54-58.
[15] Wang Yuefen,Zhu Hailing,Yan Duanwu . A Study on the Model and Application of Knowledge Integration in Intelligence Analysis[J]. 现代图书情报技术, 2006, 1(10): 43-47.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn