面向情报研究的文本语义挖掘方法述评

Semantic Text Mining Methodologies for Intelligence Analysis

【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。

[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.