Title: A Wikipedia-based semantic tensor space model for text analytics

Authors: Han-joon Kim; Jae-Young Chang

Addresses: School of Electrical and Computer Engineering, University of Seoul, 163 Seoulsiripdaero, Dongdaemun-gu, Seoul, South Korea ' Department of Computer Engineering, Hansung University, SanSunGyoRo 16, SungBukGu, Seoul, South Korea

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.

Keywords: tensor space model; vector space model; VSM; document representation; text mining; machine learning; concepts; Wikipedia; classification; clustering; similarity.

DOI: 10.1504/IJCVR.2021.115159

International Journal of Computational Vision and Robotics, 2021 Vol.11 No.3, pp.264 - 278

Received: 30 Apr 2019
Accepted: 05 Nov 2019

Published online: 06 Apr 2021 *

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