Plagiarism Detection Techniques for Arabic Script Languages: A Literature Review

Ibrahim, Ribwar Bakhtyar; Saeed, Soran; Wakil, Karzan

doi:10.24017/science.2017.3.1

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…They reviewed all publications from 2009 to 2017, as their results plagiarism detection techniques widely used for that language. Moreover, Ibrahim et al presented the techniques that used for ASLs based on their review, most techniques used for Arabic language, then Persia and Urdu languages, but there is no publication exist for the Kurdish language [6]. Moreover wide range of research in natural language processing focuses on entities.…”

Section: Introductionmentioning

confidence: 99%

Design an Accurate Algorithm for Alias Detection

Alsurori¹,

Al-Sanabani²,

Al-Hagree³

2018

IJIEEB

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An improvement in detection of alias names of an entity is an important factor in many cases like terrorist and criminal network. Accurately detecting these aliases plays a vital role in various applications. In particular, it is critical to detect the aliases that are intentionally hidden from the real identities, such as those of terrorists and frauds. Alias Detection (AD) as the name suggests, a process undertaken in order to quantify and identify different variants of single name showing up in multiple domains. This process is mainly performed by the inversion of one-to-many and many-to-one mapping. Aliases mainly occur when entities try to hide their actual names or real identities from other entities i.e.; when an object has multiple names and more than one name is used to address a single object. N-gram distance algorithm (N-DIST) have find wide applicability in the process of AD when the same is based upon orthographic and typographic variations. Kondrak approach, a popular N-DIST works well and fulfill the cause, but at the same time we uncover that (N-DIST) suffers from serious inabilities when applied to detect aliases occurring due to the transliteration of Arabic name into English. This is the area were we have tried to hammer in this paper. Effort in the paper has been streamlined in extending the N-gram distance metric measure of the approximate string matching (ASM) algorithm to make the same evolve in order to detect aliases which have their basing on typographic error. Data for our research is of the string form (names & activities from open source web pages). A comparison has been made to show the effectiveness of our adjustment to (N-DIST) by applying both forms of (N-DIST) on the above data set. As expected we come across that adjusted (A-N-DIST) works well in terms of both performance & functional efficiency when it comes to matching names based on transliteration of Arabic into English language from one domain to another.

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Section: Introductionmentioning

confidence: 99%

Design an Accurate Algorithm for Alias Detection

Alsurori¹,

Al-Sanabani²,

Al-Hagree³

2018

IJIEEB

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“…Furthermore, in-depth investigation of cross-language plagiarism detection methods on a new paper published [10]. In these literature works some systems completed for different languages especially English, Arabic, Urdu and so on but there are no works for the Kurdish language [7]. The Kurdish language used Arabic letter after changing some letters.…”

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

Plagiarism Detection System for the Kurdish Language

Wakil¹,

Ghafoor²,

Abdulrahman³

et al. 2017

IJITCS

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Abstract-One of the serious issues is plagiarism, especially in the education field. Detecting the plagiarism became a challenging task, particularly in natural language texts. In the past years, some plagiarism detection tools have been developed for diverse natural languages, mainly English. Language-independent tools exist as well but are considered as too restrictive as they usually do not consider specific language features. The problem is there is no plagiarism Detection system for the Kurdish language. In this paper, we introduce a new system for plagiarism detection for Kurdish Language, based on n-gram algorithm, our system can detect the word, phrases, and paragraphs. Moreover, our system effectiveness for detect plagiarist texts in localhost and online especially in Google search engine. This system is more useful for the academic organizations such as schools, institutes, and universities for finding copied texts from another document.

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Design and Implementation of Arabic Plagiarism Detection System

Jaber

Aliwy

2021

Intelligent Systems Reference Library

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Plagiarism Detection Techniques for Arabic Script Languages: A Literature Review

Cited by 5 publications

References 16 publications

Design an Accurate Algorithm for Alias Detection

Design an Accurate Algorithm for Alias Detection

Plagiarism Detection System for the Kurdish Language

Design and Implementation of Arabic Plagiarism Detection System

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