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Now a day?s plagiarism became very common in many fields of life such as research and education. It is an illegal deed used to make others work as own property without any proper references. Plagiarism is defined as showing other?s work as your own or using/stealing other?s ideas without any permission. Due to advancement in plagiarism techniques adopted by plagiarist, it is very difficult to detect plagiarism accurately by existing techniques. Different features are observed to determine the presence of plagiarism in documents such as syntactic, lexical, semantic and structural features. Today lots of techniques are introduced to detect plagiarism i.e. string matching, a bag of words, fingerprinting, citation analysis and stylometry . Advance detectors mostly work with source code or natural language text. To detect similarity in natural language texts, detectors commonly explore the Internet. In text analysis, detectors use very easy and simple comparison procedures based on broad coverage and processing speed. This research explores new and modern plagiarism detection tasks especially text-based plagiarism detection includes monolingual plagiarism detection. The main idea behind this research is that rewritten and original text does not have similar text and differences among these documents can be explored with the help of linguistic and statistical indicators. To investigate above statement, the main research objectives are formulated as follow; a four stage novel framework for plagiarism detection is proposed. Natural Language Processing (NLP) is used by this framework instead of focusing on traditional string-matching approaches. The objective of this model is to use text pre-processing and statistical, shallow and deep linguistic techniques using a corpus-based approach. Proposed framework is tested by comparing its working theoretically with other techniques.
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