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Register a new userPlagiarism Detection in Arabic Language using Rhetorical Structure Theory
كشف الانتحال في اللغة العربية باستخدام نظرية بنية الكلام البلاغية
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قدم هذا البحث دراسة مرجعية عن الخوارزميات و النظم المتوافرة لكشف الانتحال، إِذ صمم و بني تطبيق لكشف الانتحال باستخدام محركات البحث المتوافرة على الشبكة العنكبوتية. إن مسألة كشف الانتحال في الوثائق المكتوبة باللغات الطبيعية هي مسألة معقدة و تتعلق بخصائص اللغة الطبيعية المعنية نفسها. يوجد العديد من الخوارزميات المستخدمة لكشف الانتحال في اللغات الطبيعية التي تقسم بشكل عام إلى صنفين رئيسين هما خوارزميات المقارنة بين الملفات عن طريق بصمات الملفات، و خوارزميات مقارنة محتوى الملفات التي تتضمن خوارزميات مقارنة السلاسل النصية و خوارزميات مقارنة البنى الشجرية للملفات. تعتمد نظم كشف الانتحال على نوع محدد أو مزيج من خوارزميات كشف الانتحال؛ و ذلك للحصول على نظم كشف انتحال فعالة (سريعة و دقيقة). طور في هذا العمل نظام لكشف الانتحال باستخدام محرك البحث Bing و قد استخدمت خوارزمية تعتمد على خصائص اللغة،باستخدام نظرية بنية الكلام البلاغية (Rhetorical Structure Theory ).
This paper presents a review of available algorithms and plagiarism detection systems، and an implementation of Plagiarism Detection System using available search engines on the web. Plagiarism detection in natural language documents is a complicated problem and it is related to the characteristics of the language itself. There are many available algorithms for plagiarism detection in natural languages .Generally these algorithms belong to two main categories ; the first one is plagiarism detection algorithms based on fingerprint and the second is plagiarism detection algorithms based on content comparison and includes string matching and tree matching algorithms . Usually available systems of plagiarism detection use specific type of detection algorithms or use a mixture of detection algorithms to achieve effective detection systems (fast and accurate). In this research, a plagiarism detection system has been developed using Bing search engine and a plagiarism detection algorithm based on Rhetorical Structure Theory.
References used
Shizhong Wu; Yongle Hao; Xinyu Gao; Baojiang Cui; Ce Bian, Homology Detection Based on Abstract Syntax Tree Combined Simple Semantics Analysis, Web Intelligence and Intelligent Agent Technology (WI-IAT), vol.3, pp.410-414, 2010
Vinod K.R., Sandhya.S, Sathish Kumar D, Harani A, David Banji, Otilia JF Banji, Plagiarism-history detection and prevention, Journal for drugs and medicines, Vol.3, Issue:1, pp.1- 4, 2011
Al-Khatib B., Aspel A. ,Saleh M., fares M.، Hamad M.M., plagiarism detection using the web, Damascus university,informatics engineering college, 2007
Al-Sanie W., Towards an infrastructure for Arabic text Summarization using Rhetorical Structure Theory, master thesis , king Saud University, K.S.A., 2005
[Bing , API Basics. [online] Available at: http://www.bing.com/developers/s/APIBasics.ht ml [Accessed 15-October 2011
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This paper presents a reference study of available algorithms for plagiarism
detection and it develops semantic plagiarism detection algorithm for plagiarism detection
in medical research papers by employing the Medical Ontologies available on the
World
Wide Web.
The issue of plagiarism detection in medical research written in natural languages is
a complex issue and related exact domain of medical research.
There are many used algorithms for plagiarism detection in natural language, which
are generally divided into two main categories, the first one is comparison algorithms
between files by using fingerprints of files, and files content comparison algorithms, which
include strings matching algorithms and text and tree matching algorithms.
Recently a lot of research in the field of semantic plagiarism detection algorithms
and semantic plagiarism detection algorithms were developed basing of citation analysis
models in scientific research.
In this research a system for plagiarism detection was developed using “Bing” search
engine, where tow type of ontologies used in this system, public ontology as wordNet and
many standard international ontologies in medical domain as Diseases ontology which
contains a descriptions about diseases and definitions of it and the derivation between
diseases.
The advancement of the web and information technology has contributed to the rapid growth of digital libraries and automatic machine translation tools which easily translate texts from one language into another. These have increased the content acces
sible in different languages, which results in easily performing translated plagiarism, which are referred to as cross-language plagiarism''. Recognition of plagiarism among texts in different languages is more challenging than identifying plagiarism within a corpus written in the same language. This paper proposes a new technique for enhancing English-Arabic cross-language plagiarism detection at the sentence level. This technique is based on semantic and syntactic feature extraction using word order, word embedding and word alignment with multilingual encoders. Those features, and their combination with different machine learning (ML) algorithms, are then used in order to aid the task of classifying sentences as either plagiarized or non-plagiarized. The proposed approach has been deployed and assessed using datasets presented at SemEval-2017. Analysis of experimental data demonstrates that utilizing extracted features and their combinations with various ML classifiers achieves promising results.
In this paper we review and list, the advantages and limitations of the significant
effective techniques employed or developed in text plagiarism detection. It was found that
many of the proposed methods for plagiarism detection have a weakness poi
nts and do not
detect some types of plagiarized operations.
This paper show a survey about plagiarism detection including several important
subjects in plagiarism detection, which is plagiarism definition, plagiarism prevention and
detection, plagiarism detection systems, plagiarism detection processes and some of the
current plagiarism detection techniques.
This paper compares between different plagiarism detection algorithms, and shows
the points of weakness, and points of efficiency, and describe the power of semantic
plagiarism detection methods, and shows its efficiency in detect plagiarism cases that
another plagiarism detection algorithms don’t able to detect these cases, that semantic
plagiarism detection methods are developed to get rid of traditional weakness points for all
plagiarism detection methods have.
This paper deals with automatic detection of plagiarism in Arabic documents. We present in this paper a new idea based on the experimentation of lexical chains. The proposed method extracts those chains from original document and uses a search engine
to verify if such chains occur in other documents. The second step in our methods uses automatic translation system to translate lexical chains and verify by using search engine if those chain occurs in document in other languages. Then we compute a correlation ratio between lexical chains and lexical chains extracted from documents provided by the search engine to detect plagiarism in the original document.
We present in the end of this paper our prototype called « Alkachef » developed to detect plagiarism in Arabic document .
We describe our system that ranked first in Hope Speech Detection (HSD) shared task and fourth in Offensive Language Identification (OLI) shared task, both in Tamil language. The goal of HSD and OLI is to identify if a code-mixed comment or post cont
ains hope speech or offensive content respectively. We pre-train a transformer-based model RoBERTa using synthetically generated code-mixed data and use it in an ensemble along with their pre-trained ULMFiT model available from iNLTK.
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