  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.
