Identifying cross-lingual plagiarism using rich semantic features and deep neural networks: A study on Arabic-English plagiarism cases

Alzahrani, Salha; Aljuaid, Hanan

doi:10.1016/j.jksuci.2020.04.009

Cited by 14 publications

(9 citation statements)

References 76 publications

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“…[115]. Plagiarism detection is the process of identifying all forms of cheating in scientific papers in the academic field, whether intentionally or unintentionally [61]. The taxonomy of plagiarism detection is divided into two, namely crosslingual and mono-lingual, where the characteristic of crosslingual is the presence of language differences between the source text and the suspicious text.…”

Section: Text Plagiarismmentioning

confidence: 99%

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A Systematic Review on Semantic Role Labeling for Information Extraction in Low-Resource Data

Ariyanto,

Purwitasari,

Fatichah

2024

IEEE Access

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Challenges in the big data phenomenon arise due to the existence of unstructured text data, which is very large, comes from various sources, has various formats, and contains much noise. The complexity of unstructured text data makes it difficult to extract useful information. Therefore, a process is needed to transform it into structured data to be processed further. The information Extraction (IE) process helps to extract relationships, entities, semantic roles, and events from unstructured text data by converting them into structured output. One of IE's tasks is Semantic Role Labeling (SRL), which has a crucial function in identifying semantic roles in a sentence so that it can enrich the understanding of the text. However, much of SRL development focuses on high-resource data, especially in English. The limited development of SRL in specific low-resource languages or domains is a complex challenge. This research aims to conduct a systematic study on the development of SRL for low-resource data, both in low-resource language or domainspecific contexts. The review process was carried out systematically using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model, and 54 quality papers were obtained from the filtering process (from 2018 to 2023). We review several essential points, including (1) datasets that are often used for SRL tasks and their labeling strategies for low-resource data, (2) methods that have currently been developed for SRL tasks and learning scenarios when dealing with low-resource data, (4) evaluation metrics, (5) application of SRL tasks. This review is complemented by a discussion of issues and potential solutions for developing SRL on low-resource data to help researchers develop SRL more effectively in dealing with the challenges faced with low-resource data.

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Section: Text Plagiarismmentioning

confidence: 99%

“…Verbs in English source texts and suspicious Arabic texts can be identified using SRL. Apart from that, BabelNet can also provide a list of verbs that are similar to Arabic and English, which then calculates the similarity vector between two predicates from different languages using the wup similarity metric [61].…”

Section: Text Plagiarismmentioning

confidence: 99%

A Systematic Review on Semantic Role Labeling for Information Extraction in Low-Resource Data

Ariyanto,

Purwitasari,

Fatichah

2024

IEEE Access

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“…It consists of 547 aligned passages from 58,911 pairs from the United Nations Parallel Corpora [32], the OPUS collection of translated texts from the web [33] and King Saud University corpus [34]. We used another corpus prepared by [35] which has roughly 2085 of paraphrased translated pairs which will be used when evaluating only paraphrasing cases.…”

Section: Construction and Properties Of The Corpusmentioning

confidence: 99%

Graph transformer for cross-lingual plagiarism detection

Hourrane

Benlahmar

2022

IJ-AI

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<span lang="EN-US">The existence of vast amounts of multilingual textual data on the internet leads to cross-lingual plagiarism which becomes a serious issue in different fields such as education, science, and literature. Current cross-lingual plagiarism detection approaches usually employ syntactic and lexical properties, external machine translation systems, or finding similarities within a multilingual set of text documents. However, most of these methods are conceived for literal plagiarism such as copy and paste, and their performance is diminished when handling complex cases of plagiarism including paraphrasing. In this paper, we propose a new graph-based approach that represents text passages in different languages using knowledge graphs. We put forward a new graph structure modeling method based on the Transformer architecture that employs precise relation encoding and delivers a more efficient way for global graph representation. The mappings between the graphs are learned both in semi-supervised and unsupervised training mechanisms. The results of our experiments in Arabic–English, French–English, and Spanish–English plagiarism detection show that our graph transformer method surpasses the state-of-the-art cross-lingual plagiarism detection approaches with and without paraphrasing cases, and provides further insights on the use of knowledge graphs on a language-independent model.</span>

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“…Deep learning models, such as neural machine translation (NMT) and transformer-based architectures, have played a significant role in enhancing translation quality by capturing complex linguistic patterns and nuances [2]. Moreover, the integration of large-scale multilingual datasets and the application of techniques like transfer learning have further improved the performance of cross-language translation systems [3]. Additionally, the emergence of pre-trained language models, such as BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer), has paved the way for more contextually aware and semantically accurate translations [4].…”

Section: Introductionmentioning

confidence: 99%

Cross-Language Translation and Comparative Study of English Literature Using Machine Translation Algorithms

Shanshan Wang

2024

jes

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Cross-language processing in English literature involves the translation and analysis of literary texts from English into other languages or vice versa. This multidimensional task encompasses various aspects, including language translation, cultural adaptation, and literary interpretation. Through cross-language processing, literary works originally written in English can reach a wider audience, enabling individuals from diverse linguistic backgrounds to access and appreciate the richness of English literature. This paper presents an innovative approach to language processing tasks through the integration of Ant Swarm Domain Statistical Machine Learning (ASDS-ML). Leveraging principles of swarm intelligence and statistical learning techniques, ASDS-ML offers a robust framework for addressing challenges in language translation and classification. In the domain of translation, ASDS-ML demonstrates promising results in achieving accurate and nuanced translations across diverse language pairs, while also exhibiting adaptability to varying linguistic contexts. Furthermore, ASDS-ML showcases its effectiveness in text classification tasks, accurately categorizing instances across multiple classes with high precision and recall. In language translation tasks, ASDS-ML achieves an average BLEU score of 0.85 across multiple language pairs, outperforming baseline methods by 10%. Additionally, in text classification tasks, ASDS-ML achieves an average accuracy of 0.92 across ten different classes, surpassing existing approaches by 5%.

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Identifying cross-lingual plagiarism using rich semantic features and deep neural networks: A study on Arabic-English plagiarism cases

Cited by 14 publications

References 76 publications

A Systematic Review on Semantic Role Labeling for Information Extraction in Low-Resource Data

A Systematic Review on Semantic Role Labeling for Information Extraction in Low-Resource Data

Graph transformer for cross-lingual plagiarism detection

Cross-Language Translation and Comparative Study of English Literature Using Machine Translation Algorithms

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