A Survey of Plagiarism Detection Systems: Case of Use with English, French and Arabic Languages

• URL: http://arxiv.org/abs/2201.03423v1
• Date: Mon, 10 Jan 2022 16:11:54 GMT
• Title: A Survey of Plagiarism Detection Systems: Case of Use with English, French and Arabic Languages
• Authors: Mehdi Abdelhamid, Faical Azouaou, Sofiane Batata
• Abstract summary: This paper presents an overview of plagiarism detection systems for use in Arabic, French, and English academic and educational settings. An indepth examination of technical forms of plagiarism was also performed in the context of this study.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In academia, plagiarism is certainly not an emerging concern, but it became of a greater magnitude with the popularisation of the Internet and the ease of access to a worldwide source of content, rendering human-only intervention insufficient. Despite that, plagiarism is far from being an unaddressed problem, as computer-assisted plagiarism detection is currently an active area of research that falls within the field of Information Retrieval (IR) and Natural Language Processing (NLP). Many software solutions emerged to help fulfil this task, and this paper presents an overview of plagiarism detection systems for use in Arabic, French, and English academic and educational settings. The comparison was held between eight systems and was performed with respect to their features, usability, technical aspects, as well as their performance in detecting three levels of obfuscation from different sources: verbatim, paraphrase, and cross-language plagiarism. An indepth examination of technical forms of plagiarism was also performed in the context of this study. In addition, a survey of plagiarism typologies and classifications proposed by different authors is provided.

Related papers

• CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model Generation [132.00910067533982]
  evaluating the degree of reproduction of copyright-protected content by language models (LMs) is of significant interest to the AI and legal communities. We introduce CopyBench, a benchmark designed to measure both literal and non-literal copying in LM generations. We find that, although literal copying is relatively rare, two types of non-literal copying -- event copying and character copying -- occur even in models as small as 7B parameters.
  arXiv  Detail & Related papers  (2024-07-09T17:58:18Z)
• Understanding Cross-Lingual Alignment -- A Survey [52.572071017877704]
  Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models. We survey the literature of techniques to improve cross-lingual alignment, providing a taxonomy of methods and summarising insights from throughout the field.
  arXiv  Detail & Related papers  (2024-04-09T11:39:53Z)
• PaperCard for Reporting Machine Assistance in Academic Writing [48.33722012818687]
  ChatGPT, a question-answering system released by OpenAI in November 2022, has demonstrated a range of capabilities that could be utilised in producing academic papers. This raises critical questions surrounding the concept of authorship in academia. We propose a framework we name "PaperCard", a documentation for human authors to transparently declare the use of AI in their writing process.
  arXiv  Detail & Related papers  (2023-10-07T14:28:04Z)
• Text Similarity from Image Contents using Statistical and Semantic Analysis Techniques [0.0]
  Image Content Plagiarism Detection (ICPD) has gained importance, utilizing advanced image content processing to identify instances of plagiarism. In this paper, the system has been implemented to detect plagiarism form contents of Images such as Figures, Graphs, Tables etc. Along with statistical algorithms such as Jaccard and Cosine, introducing semantic algorithms such as LSA, BERT, WordNet outperformed in detecting efficient and accurate plagiarism.
  arXiv  Detail & Related papers  (2023-08-24T15:06:04Z)
• Verifying the Robustness of Automatic Credibility Assessment [79.08422736721764]
  Text classification methods have been widely investigated as a way to detect content of low credibility. In some cases insignificant changes in input text can mislead the models. We introduce BODEGA: a benchmark for testing both victim models and attack methods on misinformation detection tasks.
  arXiv  Detail & Related papers  (2023-03-14T16:11:47Z)
• Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach [77.61131357420201]
  We conduct a thorough and rigorous study on fairness disparities in peer review with the help of large language models (LMs) We collect, assemble, and maintain a comprehensive relational database for the International Conference on Learning Representations (ICLR) conference from 2017 to date. We postulate and study fairness disparities on multiple protective attributes of interest, including author gender, geography, author, and institutional prestige.
  arXiv  Detail & Related papers  (2022-11-07T16:19:42Z)
• Hamtajoo: A Persian Plagiarism Checker for Academic Manuscripts [0.0]
  Hamtajoo is a Persian plagiarism detection system for academic manuscripts. We describe the overall structure of the system along with the algorithms used in each stage. In order to evaluate the performance of the proposed system, we used a plagiarism detection corpus comply with the PAN standards.
  arXiv  Detail & Related papers  (2021-12-27T15:45:35Z)
• Tortured phrases: A dubious writing style emerging in science. Evidence of critical issues affecting established journals [69.76097138157816]
  Probabilistic text generators have been used to produce fake scientific papers for more than a decade. Complex AI-powered generation techniques produce texts indistinguishable from that of humans. Some websites offer to rewrite texts for free, generating gobbledegook full of tortured phrases.
  arXiv  Detail & Related papers  (2021-07-12T20:47:08Z)
• Analyzing Non-Textual Content Elements to Detect Academic Plagiarism [0.8490310884703459]
  The thesis proposes plagiarism detection approaches that implement a different concept: analyzing non-textual content in academic documents. To demonstrate the benefit of combining non-textual and text-based detection methods, the thesis describes the first plagiarism detection system that integrates the analysis of citation-based, image-based, math-based, and text-based document similarity.
  arXiv  Detail & Related papers  (2021-06-10T14:11:52Z)
• The Struggle with Academic Plagiarism: Approaches based on Semantic Similarity [0.0]
  We present a report of how semantic similarity measures can be used in the plagiarism detection task. Current software has proven to be successful, however the problem of identifying paraphrasing or obfuscation plagiarism remains unresolved.
  arXiv  Detail & Related papers  (2021-06-02T20:00:33Z)
• Taxonomy of academic plagiarism methods [0.0]
  The article defines plagiarism, explains the origin of the term, as well as plagiarism related terms. It identifies the extent of the plagiarism domain and then focuses on the plagiarism subdomain of text documents, for which it gives an overview of current classifications. The article suggests the new classification of academic plagiarism, describes sorts and methods of plagiarism, types and categories, approaches and phases of plagiarism detection, the classification of methods and algorithms for plagiarism detection.
  arXiv  Detail & Related papers  (2021-05-25T16:49:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.