Paper Info
Title
Fuzzy Semantic Plagiarism Detection.
Abstract
This paper introduces a plagiarism detection scheme based on a Fuzzy Inference System and Semantic Role Labeling (FIS-SRL). The proposed technique analyses and compares text based on a semantic allocation for each term inside the sentence. SRL offers significant advantages when generating arguments for each sentence semantically. Voting for each argument generated by the FIS in order to select important arguments is also another feature of the proposed method. It has been concluded that not all arguments in the text affect the plagiarism detection process. Therefore, only the most important arguments were selected by the FIS, and the results have been used in the similarity calculation process. Experimental tests have been applied on the PAN-PC-09 data set and the results shows that the proposed method exhibits a better performance than the available recent methods of plagiarism detection, in terms of Recall, Precision and F-measure.
Year
DOI
Venue
2012
10.1007/978-3-642-35326-0_54
ADVANCED MACHINE LEARNING TECHNOLOGIES AND APPLICATIONS
Keywords
Field
DocType
Plagiarism Detection,Semantic Similarity,Semantic Role,Fuzzy Inference System,Rule Reduction
Semantic similarity,Plagiarism detection,Voting,Computer science,Fuzzy logic,Natural language processing,Artificial intelligence,Recall,Sentence,Semantic role labeling,Fuzzy inference system
Conference
Volume
ISSN
Citations 
322
1865-0929
2
PageRank 
References 
Authors
0.38
9
4
Name
Order
Citations
PageRank
Ahmed Hamza Osman1534.45
Naomie Salim242448.23
Yogan Jaya Kumar3526.11
Albaraa Abuobieda4564.81