Title: The integration of a newly defined N-gram concept and vector space model for documents ranking

Authors: Mostafa A. Salama; Wafaa Salah

Addresses: Department of Computer Science, British University in Egypt, Cairo, Egypt ' Business Department, British University in Egypt, Cairo, Egypt

Abstract: Vector space model (VSM) is used in measuring the similarity between documents according to the frequency of common words among them. Furthermore, the N-gram concept is integrated in VSM to put into consideration the relation between common consecutive words in the documents. This approach does not consider the context and semantic dependency between nonconsecutive words existing in the same sentence. Accordingly, the approach proposed here presents a new definition of the N-gram concept as N non-consecutive words located in the same sentence, and utilises this definition in the VSM to enhance the measurement of the semantic similarity between documents. This approach measures and visualises the correlation between the words that are commonly existing together within the same sentence to enrich the analysis of domain experts. The results of the experimental work show the robustness of the proposed approach against the current ranking models.

Keywords: N-gram; vector space model; VSM; text mining.

DOI: 10.1504/IJBIDM.2019.101265

International Journal of Business Intelligence and Data Mining, 2019 Vol.15 No.2, pp.133 - 157

Received: 11 Feb 2017
Accepted: 01 May 2017

Published online: 31 May 2019 *

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