Plagiarism detection

About: Plagiarism detection is a research topic. Over the lifetime, 1790 publications have been published within this topic receiving 24740 citations.

Plagiarism Detection based on studying correlation between Author, Title and Content

Abstract: Recently, the problem of plagiarism is becoming an important issue in many debates in the fields of Education and Technology. The wide use and availability of electronic resources makes it easy for students, authors and even academic people to access and use any piece of information and embed it into his/ her own work without proper citation. The problem is raising in an exponential manner the thing which puts the education process under threat. Several tools are presented to solve the problem of automating plagiarism detection each of which has its own good and bad features, but still the traditional way of plagiarism detection through free text search using search engines is considered an accurate and free way to detect plagiarism with the only disadvantage of being a time consuming method. This research intends to present an alternative to plagiarism detection tools by automating the traditional free search process on search engines to detect plagiarism by intelligently extracting selective parts of text from the file subject to check and pass them to search engine in different forms and processing results in order to come up with a decision of committing plagiarism in a certain degree. The approach used in this paper is to make string comparison of the text with the global www, which makes it more comprehensive compared to other plagiarism tools that depend on specific databases.

Automatic Plagiarism Detection System for Specialized Corpora

Abstract: Plagiarism in the academic writing is considered one of the worst breaches of professional conduct in western society and automatic detection of it has become an important use case for Natural Language Processing research. We created a new system, AuthentiCop, aimed at detecting plagiarism instances in Computer Science academic writings. This paper focuses on designing and implementing such a system. We analyze the solutions proposed in the PAN 2011 conference and present a novel approach based on ppjoin.

PAN 2015 Shared Task on Plagiarism Detection: Evaluation of Corpora for Text Alignment ? Notebook for PAN at CLEF 2015

Abstract: In this paper we describe and evaluate the corpora submitted to the PAN 2015 shared task on plagiarism detection for text alignment. We received mono- and cross-language corpora in the following languages (pairs): English, Persian, Chinese, and Urdu-English, English-Persian. We present an independent section for each submitted corpus including statistics, discussion of the obfusca- tion techniques employed, and assessment of the corpus quality.

English-Persian Plagiarism Detection based on a Semantic Approach

Abstract: Plagiarism which is defined as “the wrongful appropriation of other writers’ or authors’ works and ideas without citing or informing them” poses a major challenge to knowledge spread publication. Plagiarism has been placed in four categories of direct, paraphrasing (rewriting), translation, and combinatory. This paper addresses translational plagiarism which is sometimes referred to as cross-lingual plagiarism. In cross-lingual translation, writers meld a translation with their own words and ideas. Based on monolingual plagiarism detection methods, this paper ultimately intends to find a way to detect cross-lingual plagiarism. A framework called Multi-Lingual Plagiarism Detection (MLPD) has been presented for cross-lingual plagiarism analysis with ultimate objective of detection of plagiarism cases. English is the reference language and Persian materials are back translated using translation tools. The data for assessment of MLPD were obtained from English-Persian Mizan parallel corpus. Apache’s Solr was also applied to record the creep of the documents and their indexation. The accuracy mean of the proposed method revealed to be 98.82% when employing highly accurate translation tools which indicate the high accuracy of the proposed method. Also, Google translation service showed the accuracy mean to be 56.9%. These tests demonstrate that improved translation tools enhance the accuracy of the proposed method.