Arabic Plagiarism Detection Using Word2Vec-Based Semantic Features and Random Forest Classification on the ExAraPlagDet Dataset

Evaluating lexical feature extraction for plagiarism detection in Arabic documents

Plagiarism detection is the task of determining whether a document contains parts from other documents by employing different styles of plagiarism, such as copying certain parts and reordering or replacing words with synonyms, without citing the original text owner. This task is important in many applications, and there are two primary types of plagiarism detection methods: external and intrinsic. Plagiarism detection in Arabic documents is challenging because of Arabic’s rich morphological features, lexical variation, and syntactic complexity, which limit the effectiveness of some detection approaches. To address these challenges, this study introduces an external plagiarism detection framework built on an artificial neural network (ANN) model and a lexical feature extraction framework adapted to the linguistic features of Arabic. The proposed framework is evaluated using ExAraPlagDet-2015 benchmark, where a baseline model using support vector machine (SVM) is introduced for comparison. Experimental results demonstrate notable improvements in plagiarism detection performance of the proposed framework compared with SVM and other baseline methods. The proposed framework provides a precision value of 92% and an F-score value of 96%, verifying its effectiveness for Arabic plagiarism detection.

A novel semantic–syntactic hybrid plagiarism detection system based on word embeddings and similarity measures

LSTM-Based Classification of Indonesian Regional Song Lyrics by Language

This study successfully proposes a Long Short-Term Memory (LSTM)-based model for automatic classification of Indonesian regional song lyrics by language. Unlike prior works that often focus on sentiment analysis or use unbalanced datasets, this research utilizes a balanced dataset consisting of 2,500 lyric segments from five regional languages: Javanese, Sundanese, Batak, Minangkabau, and Banjarese. A comprehensive preprocessing pipeline is applied, including case folding, text cleaning, tokenization, stopword removal, stemming, sequence padding, and label encoding to transform textual data into numerical representations. The model is evaluated using 5-fold cross-validation to ensure robustness and generalization across different data partitions. Experimental results show that the proposed model achieves an accuracy of 95.24%, precision of 95.36%, recall of 95.24%, and F1-score of 95.26%, indicating strong and consistent performance. These findings demonstrate that LSTM effectively captures sequential linguistic patterns and contextual relationships within regional languages, enabling accurate classification despite similarities in vocabulary and structure. Furthermore, this study contributes to the advancement of natural language processing for low-resource languages and highlights the potential of deep learning approaches in supporting the digital preservation and automatic organization of Indonesian regional cultural content.

Multilingual AI-Generated Text Detection in Arabic, English, and Turkish Using a Hybrid Transformer–Graph Convolutional Network

Detecting AI-generated text has become a critical task as artificial intelligence systems are increasingly used in content creation. Current detection methods often suffer from limited accuracy and weak multilingual performance. This problem is especially challenging in Turkish, Arabic, and English due to their distinct linguistic structures, including agglutinative morphology in Turkish, root-based morphology in Arabic, and semantic ambiguity in English. To address these challenges, this study proposes a hybrid architecture that combines a Transformer-based DistilBERT model with a Graph Convolutional Network (GCN). While DistilBERT captures rich contextual and semantic information, GCN enhances detection by modeling structural relationships within text data. The proposed model is evaluated against other well-known approaches. Experimental results show that the hybrid DistilBERTGCN framework achieves high detection accuracy, reaching 99% for English and 98% for Turkish and Arabic. In addition, this study introduces new multilingual datasets, contributing to the advancement of the literature research.

Multilingual Plagiarism Detection Using GNNs and Syntax-Semantic Knowledge Graphs

Plagiarism has become an increasingly serious concern, especially within academia, driven by the unlimited availability of online information. It refers to the uncredited use of another author’s text, images, code, graphics, or ideas. There are several types of plagiarism as direct copying, paraphrasing, reformulating, or even translating across languages. Historically, approaches for Cross Language Plagiarism Detection (CLPD) are centered on linguistic and structural cues, multilingual alignment processes, or a measurement of perceived similarity based on concepts to compare. In this study, we propose a new hybrid Approach for CLPD, which combines semantic information from WordNet with the syntactic structure from Universal Dependencies, then these relations are modeled in knowledge graphs for multiple language pairs. In detail, we utilize RotatE for relation embedding, GCN for node embedding, and GAT for improvement in learning contextual representations, to build and embed the graphs. To prove the efficiency of the new hybrid method, three approaches are compared: 1) using only WordNet to build the knowledge graph, 2) using only Universal Dependencies, and 3) the proposed hybrid approach. Our approach demonstrates clear improvements over state-of-the-art baselines across three language pairs: English-Spanish, English-French, English-Arabic, and English-German, on datasets including PAN11, JRC-Acquis, Europarl, Wikipedia, OPUS, and a curated set of conference papers. The proposed hybrid approach achieves Plagdet scores of 98.50% for English-Spanish, 89.02% for English-German, 77.65% for English-Arabic, and 77.67% for English-French.

Language Identification in Transliteration-Based Code-Mixed Text: A Study on Telugu–English Data

In recent years, social media users in multilingual regions have begun mixing languages more freely, and Telugu–English combinations are among the most common examples in India. Much of this content appears in informal Roman transliteration, and the lack of uniform spelling makes automatic processing difficult. In this work, we focus on word-level Language Identification (LID) for such transliterated text. Our approach relies on character-based TF–IDF features and a set of traditional machine-learning models. In this study, we worked with four different models—Multinomial Naive Bayes, Logistic Regression, Random Forest, and Support Vector Machine—and evaluated them on an annotated set that included Telugu, English, Named Entity, and Universal tokens. Among the four, the SVM turned out to be the strongest, reaching an accuracy of 86% along with an F1-score of 0.85. The study also brings out some practical issues with real-world transliterated text, particularly class imbalance and the wide range of spelling variations. We conclude with possible directions for improvement, including the use of neural and transformer-based models that might capture more contextual cues in future versions of this system.