文章基本信息

标题：Research Trends on Graph-Based Text Mining
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作者：Jae-Young Chang ; Il-Min Kim
期刊名称：International Journal of Software Engineering and Its Applications
印刷版ISSN：1738-9984
出版年度：2014
卷号：8
期号：4
页码：147-156
DOI：10.14257/ijseia.2014.8.4.16
出版社：SERSC
摘要：Since text mining has been assumed to apply for unformatted text (document), it is necessary to represent text with simplified models. One of the most commonly used models is the vector space model, in which text is represented as a bag of words. Recently, many researches tried to apply a graph-based text model for representing semantic relationships between words. In this paper, we surveyed research trends of graph-based text representation models for text mining. We summarized the models, their features and forecasted further researches.
关键词：Text Mining; Vector Space Model; Text Representation; Graph Model