A Wikipedia-based semantic tensor space model for text analytics. (6th April 2021)

• Record Type: Journal Article
• Authors: Kim, Han-joon; Chang, Jae-Young
• Is Part Of: International journal of computational vision and robotics. Volume 11:Number 3(2021)
• Page Start: 264
• Page End: 278
• Publication Date: 2021-04-06
• Subjects: tensor space model -- vector space model -- VSM -- document representation -- text mining -- machine learning -- concepts -- Wikipedia -- classification -- clustering -- similarity; Computer vision -- Periodicals; Robotics -- Periodicals; Artificial intelligence -- Periodicals; 006.3705
• Languages: English
• ISSNs: 1752-9131

Abstract

This paper proposes a third-order tensor space model that represents textual documents, which contains the 'concept' space independently of the 'document' and 'term' spaces. In the vector space model (VSM), a document is represented as a vector in which each dimension corresponds to a term. In contrast, the model described here represents a document as a matrix. Most current text mining algorithms only take vectors as their input, but they suffer from 'term independence' and 'loss of term senses' issues. To overcome these problems, we incorporate the 'concept' as a distinct space in the VSM. For this, it is necessary to produce the concept vector for each term that occurs in a given document, which is related to word sense disambiguation. As an external knowledge source for concept weighting, we employ the Wikipedia Encyclopedia, which has been evaluated as world knowledge and used to improve many text-mining algorithms. Through experiments using two popular document corpora, we demonstrate the superiority of the model in terms of text clustering and text classification.