Detecting translation borrowings in huge text collections using various methods

Abstract

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.