Does linguistic treatment improve external plagiarism detection method?
Low oxygen environments promotes the inter-particle interaction of citrate-stabilized AuNPs causing them to aggregate and become toxic to biological tissues
XÜLASƏ (Abstract)
Mühüm şərtlər: Məqaləniz haqqında ilkin təəssürat Nəticələrin əhəmiyyəti Yekunun qiymətlədirilməsi Məqsədin uyğunluğu Məqalənin oxuna biləcək əsas hissəsi Yazı üslubu
XÜLASƏ (davamı)
Xülasə adətən 5 cür məlumatı əhatə edir: Motivasiya Tədqiqat niyə aparılmalıdır? Məqsədlər Məqsədiniz hansı məsələni həll etməkdir. Metodlar Nə etdiniz? Nəticələr Nəyi tapdınız? Yekun Sizin tədqiqatınız bu sahə üçün niyə əhəmiyyətlidir?
XÜLASƏ (davamı)
Yaxşı yazılmış xülasə: An improved external plagiarism detection method based on linguistic treatment Abstract Plagiarism is the unauthorized use of the ideas and expression of someone else and involves representing their work as your own. Various statistical methods have been used in several external plagiarism detection systems. Most of the current systems are based on word-document co-occurrence statistics in the source documents and the suspicious documents. However, they do not consider the syntactic information, which can improve the knowledge representation and therefore lead to better performance of the system. We aim to present an external plagiarism detection method that is able to avoid selecting a source document sentence whose similarity with a suspicious document sentence is high but its meaning is different. Besides, the method includes content word expansion and candidate retrieval stages to deal with the problem of information limit and runtime complexity, which are the main issues that affect the performance and runtime of the proposed method respectively. In this paper we also present a comparative evaluation between the semantic similarity measurement method and word-order similarity measurement method. In addition we propose an effective combination schema for them. As a result, the experimental results have displayed that the proposed method is able to improve the performance compared with the participating systems in PAN-PC-11. The experimental results also displayed that the proposed method demonstrates better performance as compared to other existing techniques on PAN-PC-11 datasets. These findings suggest that the combination of semantic and syntactic information is a possible treatment to improve external plagiarism detection method.