2026· Islamic University Journal of Applied Sciences· Vol VIII, pp. 22-38· 0 citations
TL;DR
The results suggest that integrating diverse similarity measures with neural networks enhances the identification of both explicit and nuanced paraphrases, thereby supporting advancements in text analysis and plagiarism detection systems.
Abstract
This study introduces a multi-similarity neural network framework for paraphrase detection, an important task in natural language processing that identifies whether two sentences convey the same meaning using different expressions. The proposed method combines various similarity measures, such as string-based similarity, semantic similarity, and embedding-based similarity, with a deep learning classifier. The framework is structured as a three-phase pipeline: preprocessing, extraction of multiple similarity features, and classification through a neural network. It employs more than 168 string similarity algorithms, semantic measures derived from WordNet, and several pre-trained embedding models to compute similarity scores. These features are aggregated and supplied to a deep neural network to determine whether sentence pairs are paraphrases. The model was evaluated on the Microsoft Research Paraphrase Corpus (MSRP) using accuracy and F1-score as performance metrics. The experimental results indicate that the proposed framework achieves 81.74% accuracy and an F1 Score of 86.6%, surpassing several existing approaches. Overall, the results suggest that integrating diverse similarity measures with neural networks enhances the identification of both explicit and nuanced paraphrases, thereby supporting advancements in text analysis and plagiarism detection systems.
In this study, a hybrid approach to semantic text similarity combining distributed word embeddings with classical lexical similarity measures is developed. Analyzed are the limitations of modern deep learning models, namely computational overhead and weak interpretability in resource-constrained environments. Proposed is a hybrid architecture that integrates Word2Vec distributed representations with cosine and Jaccard lexical similarity metrics. Investigated is a weighted fusion mechanism that combines vector-based semantic distances with set-theoretic token overlap for robust scoring. Developed is a three-stage processing pipeline covering text preprocessing, sentence embedding generation, and similarity computation and fusion. Established is a tunable weighting parameter that experimentally balances semantic depth against lexical matching precision. Conducted are experimental evaluations on benchmark semantic textual similarity and paraphrase detection datasets using classification metrics. Determined is that the proposed hybrid model attains higher correlation with human judgment than standalone or traditional baselines. Demonstrated is a notable reduction of error rates for exact lexical matches frequently missed by vector-only models. Presented is an efficient and scalable solution that balances computational performance with semantic accuracy for practical tasks.
B. Muminov, N. Allaberganova, E. Ergashev et al.· International Conference on...· 0 citations
Experimental results indicate that the proposed approach achieves competitive performance compared to existing plagiarism detection systems, and the comparative analysis highlights the strengths and limitations of different word embedding models across datasets.
Malya Singh, Vishal Gupta· Knowledge and Information Sy...· 0 citations
This study validates the effectiveness of the BERT model in semantic similarity calculation, providing more accurate technical support for related application scenarios, and laying the foundation for subsequent model optimization and lightweighting research.
Jiachen Gao· International Conference on...· 0 citations
The findings underscore the potential of advanced NLP techniques to overcome language-specific challenges, providing a foundation for future research in multilingual plagiarism detection and enhancing the development of tools for other languages facing similar challenges.
Hanan Fawzy, Ahmad Salah, Heba El-Fiqi et al.· Informatica· 0 citations
Preliminary evidence is provided that the two-level scheme is feasible as an initial semantic-similarity indicator for plagiarism screening tool for Indonesian-language student assignment documents, with the final judgment of plagiarism remaining with the examiner.
In highly specialized and terminology-dense scientific and technical texts, existing word sense disambiguation (WSD) models struggle to adequately model the contextual semantic dependencies of polysemous words, especially in engineering domains where the same term may carry different technical meanings across contexts. To address this issue, this paper proposes a robust WSD model that integrates a bidirectional long shortterm memory network (BiLSTM ) with an attention mechanism, specifically designed for Chinese patent texts. First, a two-layer BiLSTM is used for bidirectional context modeling to capture long-range dependencies. Then, a multi-head attention mechanism uses dynamic weighting to highlight key semantic components, generating highly discriminative context vectors. Finally, a paraphrase alignment mechanism employs bilinear matching to align the context vectors with candidate paraphrase embeddings, thereby reducing semantic confusion. Experiments show that the model achieves a Top-1 accuracy of 88.6 % on high-frequency words, with an average paraphrase alignment similarity of 0.920. In perturbation tests, the average robustness index is 0.159, representing reductions of 62.7%, 53.9%, and 39.5% compared to Word2Vec+CNN, BiLSTM, and BERT, respectively. The method presented in this paper helps to enhance the accuracy and stability of word meaning recognition in technical texts, providing reliable support for knowledge mining and intelligent text processing in technical domains. Its terminology alignment is also useful for engineering corpora where antenna, wavepropagation and materials terms require context-sensitive interpretation.
L. X. Gao, T. Dong, M. H. Yang· Advanced Electromagnetics· 0 citations