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 complicat
ed 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.
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.
Statistical approaches to processing natural language text have become dominant in recent years. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations.
Proofreading is the process of checking text to detect spelling, grammatical, and semantic errors in order to correct them, proofreading the grammar and the meaning of the natural languages is considered one of the basic objectives for people who a
re interested in computational linguistics, because it becomes necessary for checking written text on the computers in multiple areas, such as proofreading emails and texts on the websites pages, it is also essential for proofreading scientific articles and researches, and it can be used to correct students' answers in the traditional e-learning exams. In addition to that the manual correction process of students' answers in the traditional way is expensive in terms of time and effort, sometimes it is error prone, and it becomes more difficult when there are large number of students, so the automatic correction process is an important step to save time and effort and it avoids errors during correcting answers in the traditional way. This research presents the stages of building Automatic Content Verification Compiler. It presents the stages of building a system which is interested in English syntax check, and it displays the stages of the lexical analysis which is considered a first stage to execute the syntax analysis, in addition to that it shows the stages of executing the syntax analysis which builds the grammatical model, this model describes the simple sentences in English, the study depends on studying grammatical structure in English, then it suggests suitable parts of this model, and it presents an application which verifies English sentences and draws derivation tree of these sentences.
