基于关联数据的类簇语义揭示模型研究

发布时间：2018-05-27 13:44

本文选题：关联数据 + 共词聚类　；参考：《数据分析与知识发现》2017年04期

【摘要】：【目的】调研基于关联数据揭示类簇内主题词间语义关系的模型和技术方法。【方法】利用Google Scholar、Springer、CNKI等检索与研究主题相关的文献,调研分析并梳理当前类簇分析和语义关系揭示相关研究,构建基于关联数据的类簇语义关系揭示模型,通过实验验证模型的有效性。【结果】实验结果表明,利用关联数据可以有效揭示主题词间语义关系,弥补传统共词聚类分析在语义方面的不足。【局限】受实验数据限制,目前揭示出的语义关系局限于上下位类关系、类与实例关系和相关关系等类型,未考虑关联数据质量问题对语义揭示结果造成的影响。【结论】提出的基于关联数据的类簇语义关系揭示模型可以有效揭示主题词间语义关系,为共词聚类结果的理解和分析提供一种新的方式。
[Abstract]:[objective] to investigate the models and technical methods of revealing the semantic relationship between the subject words in the cluster based on the relevant data. [methods] using Google Scholarry Spring Spring and CNKI to retrieve the literature related to the research topic, Through investigating and analyzing the current research on cluster analysis and semantic relation revealing, a cluster semantic relation revealing model based on association data is constructed, and the validity of the model is verified by experiments. [results] the experimental results show that, The semantic relationship between subject words can be effectively revealed by using associated data, and the semantic deficiency of traditional coterm clustering analysis can be remedied. [limitations] are limited by experimental data, and the semantic relations revealed at present are confined to the relationship between upper and lower categories. Class and instance relationships and related relationships, etc., Without considering the impact of the quality of associated data on the semantic disclosure results. [conclusion] the cluster semantic relationship revealing model based on the associated data can effectively reveal the semantic relationship between the subject words. It provides a new way to understand and analyze the result of coterm clustering.
【作者单位】：中国科学院文献情报中心;中国科学院大学;
【分类号】：G254

【相似文献】