2026· International Journal of Advanced Computer Science and Applications· 0 citations· 22 references
TL;DR
This study integrates parallel multi-kernel word-level convolutional features into conventional and hybrid deep learning models for Arabic text analysis tasks, providing a systematic within-study assessment of model sensitivity to architecture, preprocessing, and learning-rate selection.
Abstract
The complexity and morphological richness of the Arabic language pose significant challenges in natural language processing (NLP), including issues with contextual understanding and feature extraction. Traditional deep learning architectures such as CNNs, LSTMs, and GRUs often struggle to effectively model these linguistic intricacies, limiting their performance on Arabic text analysis tasks. To address these limitations, the study integrates parallel multi-kernel word-level convolutional features into conventional and hybrid deep learning models. The convolutional windows capture short contextual relationships among neighboring word tokens, while recurrent components model longer sequential evidence; the framework does not directly analyze roots, affixes, or other within-word morphological structures. These enhancements are integrated into CNN, LSTM, GRU, and hybrid architectures such as LSTM-CNN and GRU-CNN. A comprehensive evaluation was conducted across varying learning rates to assess the impact of the enhanced configurations on model performance. The results indicate competitive performance within the evaluated architectures and dataset variants, although the magnitude of improvement depends on the model and learning rate. Under their best settings, the Dynamic N-gram LSTM-CNN achieved an accuracy of 93.32%, while the Dynamic N-gram LSTM achieved 93.57%. Because previously published studies use different corpora, class configurations, preprocessing pipelines, and evaluation protocols, these results are not presented as evidence of state-of-the-art superiority. Instead, the study provides a systematic within-study assessment of model sensitivity to architecture, preprocessing, and learning-rate selection. Future directions include character- and subword-level modeling, transformer-based architectures, and domain-specific tasks such as sentiment analysis and information retrieval.
Arabic language, due to its complex morphology and richness of grammar features poses significant challenges in natural language processing (NLP). In this paper, we propose a two-stage deep learning pipeline that combines Arabic text stemming and morphological classification within a single deep learning architecture. The relationship between morphological reduction and grammatical categorization is exploited by combining character-level sequence processing with transformer-based classification. A bidirectional long short-term memory (Bi-LSTM) model is employed for Arabic stem extraction to build a sequence-to-sequence (seq2seq) stemming model named Char Stemmer. To evaluate the proposed model, a gold standard dataset consisting of 260,000 traditional Arabic words extracted from Quranic words and classical Arabic books is utilized. This dataset contains a wide range of challenging word structures suitable for robust evaluation. The Char Stemmer achieved an accuracy of 93.88% on the stemming task. The proposed model obtained 93.88% accuracy, demonstrating a 38% improvement over the best traditional stemmer, P-Stemmer. Beyond stemming, the impact of stemmers on subsequent tasks is evaluated, particularly Arabic word classification. Words are categorized into three morphological classes: noun, verb, and particle. Experimental results show that the proposed system achieved macro average precision, recall, and F1-score of 0.91, 0.89, and 0.90, respectively, with an overall classification accuracy of approximately 99%.
Azal Alaswaad, B. Minaei-Bidgoli· Bulletin of Electrical Engin...· 0 citations
This study successfully proposes a Long Short-Term Memory (LSTM)-based model for automatic classification of Indonesian regional song lyrics by language, demonstrating that LSTM effectively captures sequential linguistic patterns and contextual relationships within regional languages.
Muhammad Rizky, Anandita Priatama, Aviv Yuniar Rahman et al.· Buana Information Technology...· 0 citations
A hybrid architecture that combines a Transformer-based DistilBERT model with a Graph Convolutional Network (GCN) that enhances detection by modeling structural relationships within text data is proposed.
With the vast amount of user-generated content on social media and online platforms, sentiment analysis has emerged as
an important research field in the domain of Natural Language Processing (NLP). Although moderate success has been
obtained by traditional machine learning or deep learning approaches in text sentiment classification, they are not very
good in capturing the semantics in the context, the sarcasm and the informal language patterns used in social media text.
To overcome these drawbacks, this research introduces a RoBERTa based approach for sentiment classification, which
leverages transformer-based contextual embeddings to better understand and classify sentiment. The proposed
methodology includes data pre-processing, Byte-Pair Encoding tokenization, transfer learning and fine-tuning of a pretrained model RoBERTa on a balanced sentiment dataset of textual and sentiment samples. Standard performance metrics
such as accuracy, precision, recall and F1-score are used to evaluate the model. The experimental results presented show
that the proposed framework using RoBERTa outperforms the conventional NLP methods in terms of sentiment
classification performance and interpretation ability. RoBERTa's bidirectional attention mechanism, optimized pretraining
strategy, allows for efficient processing of semantic relationships, social media sentiment and noisy text. The study also
examines the pros and cons of sentiment analysis systems based on transformers and their potential for future
development. Overall, the research proves that RoBERTa is a very powerful and reliable model for the current sentiment
classification problems and can be a major step towards developing intelligent opinion mining and automated text analysis
systems.
Vemula Vandana, K. Ushamahalaxmi, Dr. L Jagadeesh Naik· International Journal of Dru...· 0 citations
In the context of the increasing demand for cross-border communication and multilingual text processing, the complex word forms such as case, number, gender, and verb conjugation in German pose significant challenges for large-scale automatic analysis. Traditional rule-based and dictionary-driven methods struggle to balance accuracy and generalization capabilities. This paper addresses the task of automatic identification of German word forms and constructs an end-to-end model framework based on deep neural networks. It employs character-level and subword-level dual-channel feature representations, combines encoder-decoder architecture, scaled dot-product attention, and positional encoding, and jointly models long-distance dependencies within words and fine-grained morphological patterns. At the same time, it designs multi-task learning objectives incorporating morphological constraints such as case, number, and gender, and completes model training using the Adam optimizer, learning rate decay, and early stopping strategies. Experimental results show that on the standard test set, the word form accuracy rate reaches 0.917, the character accuracy rate reaches 0.966, and the average edit distance drops to 0.28. In robust scenarios such as 10% character perturbation and domain transfer, the word form accuracy rates remain at 0.887 and 0.868 respectively, with the average edit distance remaining at most 0.43, and the performance decline is within an acceptable range. This has engineering application value for the automatic processing of complex morphological languages and the construction of multilingual natural language processing systems.
Bozhen Wang· International Conference on...· 0 citations