CSS-HDC: Hierarchical Document Classification by conceptual and semantic similarities
V.Prathiba Dr. S. V. Ramana, Ekambaram Kesavulu Reddy · 2014
The victimization of syntactic components and semantic environment has constantly become a noteworthy issue in the milieu of data mining and information retrieval, which is in particular of text data. The effectiveness of this issue has delivered noticeably in absolutely unique tasks, such that as supervised learning of the text data. So significantly, still, extra syntactic or semantic info has become utilized only distinctively. With motivation gained from our earlier work that successfully able to define the concept labels for supervised learning, here in this paper we devise a hierarchical document categorization by conceptual and semantic relevance. The conceptual relevance is verified by concept labeling approach that devised in our earlier research article. Semantic relevance is explored by estimating the correlation between concept categories based on the activity labeling, which is main contribution of this paper. The results explored in empirical study concluding that the devised model is promising the significant classification by conceptual semantic relevance of given documents.