Detecting translation borrowings in huge text collections using various methods
الباحث الأول:
Adel Al-janabi
الباحثين الآخرين:
Ehsan Ali Kareem
Baqer M. Merzah
المجلة:
Indonesian Journal of Electrical Engineering and Computer Science
تاريخ النشر:
3 يونيو، 2023
مختصر البحث:
The purpose of this work is to investigate the problem of detecting transportable borrowings and text reuse. The article proposes a monolingual solution to this problem: translating the suspicious material into language collections for additional mo…
The purpose of this work is to investigate the problem of detecting transportable borrowings and text reuse. The article proposes a monolingual solution to this problem: translating the suspicious material into language collections for additional monolingual analysis. One of the major requirements for the suggested technique is robustness against machine learning ambiguities. The next step in the document analysis is split into two parts. The authors begin by retrieving documents-candidates that are similarity to other types of text recurrence. The paper proposes retrieving texts utilizing word clusters formed using distributional semantic for robustness. In the second stage, the authors use deep learning neural networks to compare the suspected document to candidates utilizing phrase embedding. The experimentation is carried out for the language pair “English-Arabic” on both articles and synthetic data.
