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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Computational Vision and Robotics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
