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Department of Informatics, College of Computers and Information Sciences, King Saud University, Riad, P. O. Box 89638, Saudi Arabia
Department of Informatics, College of Computers and Information Sciences, King Saud University, Riad, P. O. Box 89638, Saudi Arabia
Paraphrave identification is a problem of natural speech processing (NLP), which contains the determination of whether two text segments have the same meaning. Various NLP applications are rely on a solution to this problem, including automatic plagiarization detection, text summarization, machine translation (MT) and the question that is answered. The methods of identifying paraphrases found in the literature fall within two main classes: similarity-based methods and classification methods. This paper presents a critical study and evaluation of existing methods for paraphrase identification and its application to automatic plagiarism detection. It presents the classes of paraphrase phenomena, the main methods and the sets of features used by particular methods. All methods used and functions are discussed and listed in a table for a simple comparison. Their performances on the benchmark corpora are also discussed and compared via tables. The automatic plagiarism detection is displayed as an application of paraphrase identification. The benefits on the benchmark corpora of existing plagiarism recognition systems that can recognize paraphrases are compared and discussed. The main result of this study is the identification of word overlap, structural representations and MT measures as feature subset, which leads to the best performance results for support vector machines in paraphrase identification and plagiarism recognition on corpora. The performance results achieved by deep learning techniques show that these techniques are the promising research direction in this area.
Tags: paraphrasing, automatic
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