Comparative Analysis of Semantic Similarity Word Embedding Techniques for Paraphrase Detection

Aggarwal, Preeti; Kaur, Ravreet

doi:10.1007/978-981-19-0284-0_2

Cited by 5 publications

(2 citation statements)

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“…This approach allows you to effectively search for exact text borrowings but does not allow you to detect borrowings with a large proportion of paraphrased text or with inserts of text translated from another language. There are several approaches that describe the problem of finding translated borrowings for some pairs of languages [4], [5], for example, for the Spanish-English pair. This work is devoted to the detection of translated borrowings for Arabic-English pairs of languages.…”

Section: Introductionmentioning

confidence: 99%

“…Several works devoted to the search for translated borrowings use additional resources, such as thesauri and ontologies. The authors suggest using knowledge bases to extract information about the proximity between texts [4], [5]. In work Kaliappan et al [5], an algorithm based on a combination of neural networks and knowledge graphs is proposed.…”

Section: Introductionmentioning

confidence: 99%

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Detecting translation borrowings in huge text collections using various methods

Al-Janabi

Al-Zubaidi

Merzah

2023

IJEECS

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%