Review on Multiple Plagiarism: A Performance Comparison Study

TL;DR

This paper surveys various plagiarism detection methods in NLP, comparing their accuracy and effectiveness, and proposes a new approach based on sentence and word analysis using synonyms.

Contribution

It provides a comprehensive comparison of existing plagiarism detection algorithms and introduces a novel method utilizing sentence and word separation with synonyms.

Findings

Some algorithms outperform others in accuracy

Sentence and word-based methods show promising results

The proposed method enhances detection effectiveness

Abstract

Plagiarism is the practice of claiming to be someone else content, thoughts or ideas as one own without any proper credit and citations. This paper is a survey paper that, represent the some of the great research paper and its comparison that is work done on plagiarism. Now a days, plagiarism became one of the most interesting and crucial research points in Natural Language Processing area. We review some old research paper based on different types of plagiarism detection and their models and algorithm, and comparison of the accuracy of those papers. There are many several ways which are available for plagiarism detection in different language. There are a few algorithms to detecting plagiarism. Like, corpus, CL-CNG, LSI, Levenshtein Distance etc. We analysis those papers, and learn that they used different types of algorithms for detecting plagiarism. After experiment those papers, we got that some of the algorithms give a better output and accuracy for detecting plagiarism. We are going to give a review on some papers about Plagiarism and will discuss about the pros and cons of their models. And we also show a propose method for plagiarism detection method which based on sentience separation, word separation and make sentence based on synonym and compare with any sources.