Plagiarism is a widely spread problem that is the main focus of interest these days. The main objective of this work is the application of Latent Semantic Analysis (LSA) framework in the field of written-text plagiarism detection. This particular field faces various issues that are discussed thoroughly. In order to infer the latent semantics from the given text, Singular Value Decomposition (SVD) is employed for the purpose of large statistical computations. To overcome issues connected with a large amount of extracted N-grams from the text, a feature selection and subsequently a random indexing techniques are applied. Moreover, this thesis deals with the influence of text pre-processing on the accuracy of plagiarism detection. Simultaneously, the aspects of multilingual environment are explored. Various approaches in common use are discussed and compared with the new proposed method.
Automatic Plagiarism Detection Based on Latent Semantic Analysis
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Plagiarism is a widely spread problem that is the main focus of interest these days. The main objective of this work is the application of Latent Semantic Analysis (LSA) framework in the field of written-text plagiarism detection. This particular field faces various issues that are discussed thoroughly. In order to infer the latent semantics from the given text, Singular Value Decomposition (SVD) is employed for the purpose of large statistical computations. To overcome issues connected with a large amount of extracted N-grams from the text, a feature selection and subsequently a random indexing techniques are applied. Moreover, this thesis deals with the influence of text pre-processing on the accuracy of plagiarism detection. Simultaneously, the aspects of multilingual environment are explored. Various approaches in common use are discussed and compared with the new proposed method.
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