HYPLAG: Hybrid Arabic Text Plagiarism Detection System

Survey research is appropriate and necessary to address certain research question types. This paper aims to provide a general overview of the textual similarity in the literature. Measuring textual similarity tends to have an increasingly important turn in related topics like text classification, recovery of specific information from data, clustering, topic retrieval, subject tracking, question answering, essay grading, summarization, and the nowadays trending Conversational Agents (CA), which is a program deals with humans through natural language conversation. Finding the similarity between terms is the essential portion of textual similarity, then used as a major phase for sentence-level, paragraph-level, and script-level similarities. In particular, we concern with textual similarity in Arabic. In the Arabic Language, applying Natural language Processing (NLP) tasks are very challenging indeed as it has many characteristics, which are considered as confrontations. However, many approaches for measuring textual similarity have been presented for Arabic text reviewed and compared in this paper.

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“…Bilal Ghanem et al [35] 2018 The experiments showed that the model improved the accuracy by 95.7% compared to the previous baseline models.…”

Section: Literature Reviewmentioning

confidence: 97%

Textual Similarity Measurement Approaches: A Survey (1)

Abo-Elghit

Al-Zoghby

Hamza

2020

The Egyptian Journal of Language Engineering

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“…Although the works of Ghanem et al (2018) and Khorsi et al (2018) seem promising, they both have been tested on ExAraDet-2015 corpus, which is an Arabic corpus made of short sentences constructed for the PAN@FIRE plagiarism detection competition. We suspect this corpus might not be suitable for academic plagiarism detection as it is not a well-organized academic corpus, neither it is discourse-structure annotated.…”

Section: Related Workmentioning

confidence: 99%

An academic Arabic corpus for plagiarism detection: design, construction and experimentation

Al-Thwaib

Hammo

Yagi

2020

Int J Educ Technol High Educ

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Advancement in information technology has resulted in massive textual material that is open to appropriation. Due to researchers' misconduct, a plethora of plagiarism detection (PD) systems have been developed. However, most PD systems on the market do not support the Arabic language. In this paper, we discuss the design and construction of an Arabic PD reference corpus that is dedicated to academic language. It consists of (2312) dissertations that were defended by postgraduate students at the University of Jordan (JU) between the years 2001-2016. This Academic Jordan University Plagiarism Detection corpus; henceforth, JUPlag, follows the Dewey decimal classification (DDC) in the way it is structured. The goal of the corpus is twofold: Firstly, it is a database for the detection of plagiarism in student assignments, reports, and dissertations. Secondly, the n-gram structure of the corpus provides a knowledgebase for linguistic analysis, language teaching, and the learning of plagiarism-free writing. The PD system is guided by JU Library's metadata for retrieval and discovery of plagiarism. To test JUPlag, we injected an unseen dissertation with multiple instances of plagiarism-simulated paragraphs and sentences. Experimentation with the system using different verbatim n-gram segments is indeed promising. Preliminary results encourage that permission be sought to enrich this corpus with all the theses in the Thesis Repository of the Union of Arab Universities. The JUPlag corpus is intended to function as an indispensable source for testing and evaluating plagiarism detection techniques. Since the University of Jordan is seeking to become a center for plagiarism detection for Arabic content and being a non-profit organization, it will charge a nominal fee for the use of JUPlag to finance the maintenance and development of the corpus.

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“…The sentence embeddings is obtained by averaging its words embeddings. Previously in (Ghanem et al, 2018a), the authors showed that using the main sentence components (verbs, nouns, and adjectives) improved the detection accuracy of a plagiarism detection approach 3 rather than using the full sentence components. Therefore, we build these embeddings vectors using the main sentence components.…”

Section: Featuresmentioning

confidence: 99%

Stance Detection in Fake News A Combined Feature Representation

Ghanem¹,

Rosso²,

Rangel³

2018

Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)

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With the uncontrolled increasing of fake news and rumors over the Web, different approaches have been proposed to address the problem. In this paper, we present an approach that combines lexical, word embeddings and n-gram features to detect the stance in fake news. Our approach has been tested on the Fake News Challenge (FNC-1) dataset. Given a news title-article pair, the FNC-1 task aims at determining the relevance of the article and the title. Our proposed approach has achieved an accurate result (59.6 % Macro F1) that is close to the state-of-the-art result with 0.013 difference using a simple feature representation. Furthermore, we have investigated the importance of different lexicons in the detection of the classification labels.

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HYPLAG: Hybrid Arabic Text Plagiarism Detection System

Cited by 9 publications

References 7 publications

Textual Similarity Measurement Approaches: A Survey (1)

Textual Similarity Measurement Approaches: A Survey (1)

An academic Arabic corpus for plagiarism detection: design, construction and experimentation

Stance Detection in Fake News A Combined Feature Representation

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