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现代图书情报技术  2016, Vol. 32 Issue (10): 13-24     https://doi.org/10.11925/infotech.1003-3513.2016.10.02
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面向情报研究的文本语义挖掘方法述评*
赵冬晓(),王效岳,白如江,刘自强
山东理工大学科技信息研究所 淄博 255049
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
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摘要 

目的】对主要的文本语义挖掘方法及其在情报研究中的应用进行综述分析。【文献范围】集中选择近10年国内外主流的文本语义挖掘方法在情报研究领域的应用以及少数此前的代表性研究和文本语义挖掘方法的进展研究。【方法】分别概括介绍词、句子和篇章粒度的文本语义挖掘方法、算法, 并通过主题演化和技术挖掘领域的实际应用进行方法剖析。【结果】文本语义挖掘方法与传统的情报分析方法相比, 主要弥补了两个缺陷: 侧重于分析结构化的数据, 无法处理多种异构的数据源; 分析停留在统计语法层面, 没有深入到文本的语义信息。【局限】仅对主流的文本语义挖掘方法以及在科学研究领域的应用进行综述分析, 研究不全面。【结论】文本语义挖掘方法弥补了传统情报分析方法的不足, 是情报研究方法的重要发展方向, 随着方法的成熟, 下一步研究重点是外部语义资源的丰富。

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赵冬晓
王效岳
白如江
刘自强
关键词 文本语义挖掘情报分析主题演化技术挖掘    
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
收稿日期: 2016-06-06      出版日期: 2016-11-23
基金资助:*本文系国家社会科学基金一般项目“未来新兴科学研究前沿识别研究”(项目编号: 16BTQ083)的研究成果之一
引用本文:   
赵冬晓,王效岳,白如江,刘自强. 面向情报研究的文本语义挖掘方法述评*[J]. 现代图书情报技术, 2016, 32(10): 13-24.
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.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2016.10.02      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2016/V32/I10/13
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