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Paraphrase plagiarism is one of the difficult challenges facing plagiarism detection systems. Paraphrasing occur when texts are lexically or syntactically altered to look different, but retain their original meaning. Most plagiarism detection systems (many of which are commercial based) are designed to detect word co-occurrences and light modifications, but are unable to detect severe semantic and structural alterations such as what is seen in many academic documents. Hence many paraphrase plagiarism cases go undetected. In this paper, we approached the problem of paraphrase plagiarism by proposing methods for detecting the most common techniques (phenomena) used in paraphrasing texts (namely; lexical substitution, insertion/deletion and word and phrase reordering), and combined the methods into a paraphrase detection model. We evaluated our proposed methods and model on collections containing paraphrase texts. Experimental results show significant improvement in performance when the methods were combined (the proposed model) as opposed to running them individually. The results also show that the proposed paraphrase detection model outperformed a standard baseline (based on greedy string tilling), and previous studies.

Cross-lingual plagiarism (CLP) occurs when texts written in one language are translated into a different language and used without acknowledging the original sources. One of the most common methods for detecting CLP requires online machine translators (such as Google or Microsoft translate) which are not always available, and given that plagiarism detection typically involves large document comparison, the amount of translations required would overwhelm an online machine translator, especially when detecting plagiarism over the web. In addition, when translated texts are replaced with their synonyms, using online machine translators to detect CLP would result in poor performance. This paper addresses the problem of cross-lingual plagiarism detection (CLPD) by proposing a model that uses simulated word embeddings to reproduce the predictions of an online machine translator (Google translate) when detecting CLP. The simulated embeddings comprise of translated words in different languages mapped in a common space, and replicated to increase the prediction probability of retrieving the translations of a word (and their synonyms) from the model. Unlike most existing models, the proposed model does not require parallel corpora, and accommodates multiple languages (multi-lingual). We demonstrated the effectiveness of the proposed model in detecting CLP in standard datasets that contain CLP cases, and evaluated its performance against a state-of-the-art baseline that relies on online machine translator (T+MA model). Evaluation results revealed that the proposed model is not only effective in detecting CLP, it outperformed the baseline. The results indicate that CLP could be detected with state-of-the-art performances by leveraging the prediction accuracy of an internet translator with word embeddings, without relying on internet translators., Comment: This is a mildly edited version that is currently undergoing review