Methods for Detecting Paraphrase Plagiarism

TL;DR

This paper presents new methods for detecting paraphrase plagiarism by addressing lexical, syntactic, and semantic alterations, and combines them into a model that outperforms existing baseline and previous approaches.

Contribution

It introduces a combined paraphrase detection model that effectively identifies various paraphrasing phenomena, improving detection accuracy over existing methods.

Findings

Significant performance improvement with combined methods

Outperforms baseline greedy string tilling approach

Achieves better results than previous studies

Abstract

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.