An Automatic Similarity Detection Engine Between Sacred Texts Using Text Mining and Similarity Measu...

مرکز و کتابخانه مطالعات اسلامی به زبان های اروپایی

منو

" An Automatic Similarity Detection Engine Between Sacred Texts Using Text Mining and Similarity Measures "

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	802868
Doc. No	:	TL48047
Call number	:	‭1641125320;‮ ‬1570850‬
Main Entry	:	Sookdial, Vijay T.
Title & Author	:	An Automatic Similarity Detection Engine Between Sacred Texts Using Text Mining and Similarity Measures
	:	\ Salha Hassan Muhammed Qahl
	:	Fokoue, Ernest
College	:	Rochester Institute of Technology
Date	:	2014
Degree	:	M.S.
student score	:	2014
field of study	:	Applied Statistics
Page No	:	104
Note	:	Committee members: Chen, Linlin; Parody, Robert
Note	:	Place of publication: United States, Ann Arbor; ISBN=978-1-321-40085-4
Abstract	:	Is there any similarity between the contexts of the Holy Bible and the Holy Quran, and can this be proven mathematically? The purpose of this research is using the Bible and the Quran as our corpus, we explore the performance of various feature extraction and machine learning techniques. The unstructured nature of text data adds an extra layer of complexity in the feature extraction task, and the inherently sparse nature of the corresponding data matrices makes text mining a distinctly difficult task. Among other things, We assess the difference between domain-based syntactic feature extraction and domain-free feature extraction, and then use a variety of similarity measures like Euclidean, Hillinger, Manhattan, cosine, Bhattacharyya, symmetries kullback-leibler, Jensen Shannon, probabilistic chi-square and clark. For a similarity to identify similarities and differences between sacred texts.
Subject	:	Mathematics; Statistics; Computer science
Descriptor	:	Pure sciences;Applied sciences;Data mining;Machine learning;Sacred texts;Similarity measures
Added Entry	:	Fokoue, Ernest
Added Entry	:	Rochester Institute of Technology
	:	Applied Statistics