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Using Text's Terms and Syntactical Properties for Document Similarity
International Journal of Intelligent Information Systems
Volume 5, Issue 6, December 2016, Pages: 82-87
Received: Oct. 4, 2016; Accepted: Nov. 5, 2016; Published: Dec. 5, 2016
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Author
Mohamed Taybe Elhadi, Department of Computer Science, University of Zawia, Zawia, Libya
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Abstract
This paper reports on experiments performed to investigate the use of syntactical structures of sentences combined with sentences' terms for document similarity calculation. The document's sentences were first converted into ordered Part of Speech (POS) tags that were then fed into the Longest Common Subsequence (LCS) algorithm to determine the size and count of the LCSs found when comparing the document sentence by sentence. As a first stage, these syntactical features of the text were used as a structural representation of the document’s text. However, the produced strings of tags not only work as text representative but also provide for text size reduction. This improves the processing efficiency of comparing the document's representative strings using the LCS. A score is generated by computing an accumulative value based on the number of the LCSs found. In the second stage, documents that score well in the first stage are subjected to further comparison using the actual words of the sentences (content) in a sentence by sentence fashion. An overall final is generated as a measure of similarity using the common words (accumulated for the whole document) and the total number of LCSs from the first step. Experiments were done on two different corpora. Results obtained have showed the utility of the proposed procedure in calculating similarities between written documents. The overall discrimination power was maintained while the size of the documents was reduced using only a representative of the document based on the tagged string.
Keywords
Syntactical Structures, Document Similarity, Bag-of-words, Longest Common Subsequence
To cite this article
Mohamed Taybe Elhadi, Using Text's Terms and Syntactical Properties for Document Similarity, International Journal of Intelligent Information Systems. Vol. 5, No. 6, 2016, pp. 82-87. doi: 10.11648/j.ijiis.20160506.11
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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