A Narrative Method for Evaluating Documents Similarity based on Unique Strings
Phan Hieu Ho1, Trung Hung Vo2, Ngoc Anh Thi Nguyen3, Ha Huy Cuong Nguyen4
1Phan Hieu Ho, The University of Danang, Danang City, Vietnam.
2Trung Hung Vo, The University of Danang, Danang City, Vietnam.
3Ngoc Anh Thi Nguyen, The University of Danang- University of Education and Science, Danang City, Vietnam.
4Ha Huy Cuong Nguyen, Quang Nam University, Tam Ky, Vietnam.
Manuscript received on 11 October 2019 | Revised Manuscript received on 20 October 2019 | Manuscript Published on 02 November 2019 | PP: 473-479 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B10730982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1073.0982S1119
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A precision and efficiency model of the similarity computing of texts plays an important key of duplicate documents detection. In this paper, we focus on presenting and evaluating documents similarity based on a new method viaen coding text into unique strings, called Deoxyribo Nucleic Acid (DNA) sequences. Additionally, the proposed method including an algorithm for marking as well as coloring similar paragraphs in the test document compared to other documents available in the data warehouse and developing a system for copy detection are investigated. Experimental results show that this novel approach is highly accurate for areal dataset taken from PAN. The results corroborate the advantages of the novel approach with average of 99%accuracyfor the text similarity detection with a selection threshold of ε=10-12.The results of this study are applied to implement a practical system for evaluating documents similarity at the University of Danang, Vietnam.
Keywords: Text Similarity, Text Encoding, DNA Sequencing, Text Coloring, Copy Detection.
Scope of the Article: Text Mining