Arabic Plagiarism Detection Using Word Correlation in N-Grams with K-Overlapping Approach, Working Notes for PAN-AraPlagDet at FIRE 2015

This report explains our Arabic plagiarism detection system which we used to submit our run to AraPlagDetect competition at FIRE 2015. The system was constructed through four main stages. First is pre-processing which includes tokenisation and stop words removing. Second is retrieving a list of candidate documents for each suspicious document using K-gram fingerprinting and Jaccard coefficient. Suspicious documents are then compared indepth with the associated candidate documents. This stage entails the computation of the similarity between constructed N-grams with K-overlapping where N and K were experimentally assigned to 8 and 3, respectively. The similarity between N-Gram pairs were computed based on word correlations. Each word was compared with words in candidate N-Gram and correlated by 1 if they are matched. Correlation values were averaged then compared to a threshold. The last step is post-processing whereby consecutive N-Grams were joined to form united plagiarised segments. Our performance measures on the training corpus were encouraging (recall=0.829, precision=0.843, granularity=1.11). The recall measure on the test collection was unfortunately less (recall= 0.530) but precision and granularity remained consistent with the train set (precision= 0.831, granularity= 1.18). This drop in recall may be due to the fact that our candidate retrieval stage retrieves only documents which share copied fragments but there exist plagiarised documents which have no exact-copied cases. Although this system can detect some means of obfuscation such as restructuring or rewording of few phrases, it might not work with handmade paraphrases. Our future work is to advance the candidate retrieval stage and contain semantic-based metrics in the detection stage.