Towards perfect text classification with Wikipedia-based semantic Naïve Bayes learning

Abstract

Abstract This paper suggests a novel way of dramatically improving the Naive Bayes text classifier with our semantic tensor space model for document representation. In our work, we intend to achieve a perfect text classification with the semantic Naive Bayes learning that incorporates the semantic concept features into term feature statistics; for this, the Naive Bayes learning is semantically augmented under the tensor space model where the ‘concept’ space is regarded as an independent space equated with the ‘term’ and ‘document’ spaces, and it is produced with concept-level informative Wikipedia pages associated with a given document corpus. Through extensive experiments using three popular document corpora including Reuters-21578, 20Newsgroups, and OHSUMED corpora, we prove that the proposed method not only has superiority over the recent deep learning-based classification methods but also shows nearly perfect classification performance.