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Electronics
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Natural Language Processing: From Deep Learning to Real-World Solutions
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Natural Language Processing: From Deep Learning to Real-World Solutions

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 March 2027 | Viewed by 2966

Editors

Dr. Ivelina Stoyanova

E-Mail Website
Guest Editor
Department of Computational Linguistics, Institute for Bulgarian Language, Sofia 1113, Bulgaria
Interests: computational linguistics; natural language processing; computational lexicons; lexical–semantic networks; semantic relations; linguistic annotation; information extraction; corpus linguistics
Dr. Svetlozara Leseva

E-Mail Website
Guest Editor
Department of Computational Linguistics, Institute for Bulgarian Language, Sofia 1113, Bulgaria
Interests: corpus linguistics; computational linguistics; natural language processing; computational lexicons; semantic analysis

Special Issue Information

Dear Colleagues,

In recent years, we have witnessed a significant advancement in the field of natural language processing with the extensive use of machine learning and deep learning and the increasing implementation of large language models. Designing efficient systems for language understanding and generation is of high priority in addressing real-world challenges and for various domains such as healthcare, finance, science, education, social care, etc.

We invite researchers and practitioners from both academia and industry working in different fields such as natural language processing, engineering, computer science, data mining, computational linguistics, and other related areas to submit novel research that focuses on language technologies and their applications to solving real-world problems.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following: machine learning, deep learning, large language models, multimodal NLP, multilingual and cross-lingual NLP, and real-world applications of language technologies in specific domains.

We look forward to receiving your contributions.

Dr. Ivelina Stoyanova
Dr. Svetlozara Leseva
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • language technologies
  • natural language processing
  • machine learning
  • deep learning
  • large language models
  • computational linguistics

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Published Papers (3 papers)

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Research

17 pages, 1031 KB  
Article
Augmented Disentanglement and Aggregation for Nested Named Entity Recognition
by Jinjin Zhang, Kun Zhang, Chengliang Zhong and Ruhan A
Electronics 2026, 15(13), 2840; https://doi.org/10.3390/electronics15132840 - 29 Jun 2026
Viewed by 183
Abstract
Nested named entity recognition (Nested NER) aims to identify and classify all possible span entities within a text. Existing approaches primarily rely on enumeration techniques and span-based methods to address the challenge of overlapping entities. However, these methods often overlook the structural distribution [...] Read more.
Nested named entity recognition (Nested NER) aims to identify and classify all possible span entities within a text. Existing approaches primarily rely on enumeration techniques and span-based methods to address the challenge of overlapping entities. However, these methods often overlook the structural distribution and inherent semantics of entities, making them susceptible to issues such as ambiguous start-end tokens, blurred entity boundaries, and a high degree of token overlap. In this paper, we propose a novel strategy we name Augmented Disentanglement and Aggregation for Nested Named Entity Recognition (ada-NER), which employs a series of augmentation strategies to extract nested entities from text. Specifically, we first reformulate the nested NER task as a problem of disentangling and aggregating the relationships between span recognition and type classification. This formulation enables the model to capture fine-grained and comprehensive contextual interactions within sentences. Furthermore, span entities are recognized through the joint modeling of hard boundary encoding and soft edge encoding, while type classification is enhanced by incorporating both intra and inter distribution relationships as well as dependency information. Finally, we introduce a well-designed fusion mechanism to obtain entity representation within a shared space. Extensive experiments on two public datasets demonstrate the effectiveness of our proposed model, which consistently outperforms competitive baseline methods. Full article
(This article belongs to the Special Issue Natural Language Processing: From Deep Learning to Real-World Solutions)
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16 pages, 412 KB  
Article
Exploring the Effects of Data Volume and Transfer-Language Choice on Transfer Learning with Application to Polish
by Juuso Eronen, Zhenzhen Liu, Michal Ptaszynski, Karol Nowakowski and Fumito Masui
Electronics 2026, 15(11), 2254; https://doi.org/10.3390/electronics15112254 - 22 May 2026
Viewed by 272
Abstract
Transfer learning offers a practical way to improve neural machine translation in low-resource settings, but its effectiveness depends on both the choice of transfer language and the amount of target-language data available for adaptation. In this study, we examine these factors specifically for [...] Read more.
Transfer learning offers a practical way to improve neural machine translation in low-resource settings, but its effectiveness depends on both the choice of transfer language and the amount of target-language data available for adaptation. In this study, we examine these factors specifically for Polish–English translation using mBART. We evaluate Czech, Russian, and German as parent languages and extend the analysis with a combined Slavic parent model trained on Czech and Russian. The models are compared across 0-shot, 10-shot, 100-shot, 1k-shot, and 10k-shot settings. Within this Polish–English mBART setting, Czech provides the strongest zero-shot performance, while Russian and German improve substantially as Polish fine-tuning data increases and achieve the strongest results at higher shot levels. The paper therefore analyzes selected transfer-language configurations rather than a formally measured similarity variable. The results suggest that, in this setup, transfer-language choice matters most when no Polish supervision is available, whereas larger amounts of Polish data can compensate for weaker initial transfer alignment. Full article
(This article belongs to the Special Issue Natural Language Processing: From Deep Learning to Real-World Solutions)
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37 pages, 3329 KB  
Article
Deobfuscating Iraqi Arabic Leetspeak for Hate Speech Detection Using AraBERT and Hierarchical Attention Network (HAN)
by Dheyauldeen Marzoog and Hasan Çakir
Electronics 2025, 14(21), 4318; https://doi.org/10.3390/electronics14214318 - 3 Nov 2025
Viewed by 1988
Abstract
The widespread use of leetspeak and dialectal Arabic on social media poses a critical challenge to automated hate speech detection systems. Existing Arabic NLP models, largely trained on Modern Standard Arabic (MSA), struggle with obfuscated, noisy, and dialect-specific text, leading to poor generalization [...] Read more.
The widespread use of leetspeak and dialectal Arabic on social media poses a critical challenge to automated hate speech detection systems. Existing Arabic NLP models, largely trained on Modern Standard Arabic (MSA), struggle with obfuscated, noisy, and dialect-specific text, leading to poor generalization in real-world scenarios. This study introduces a Hybrid AraBERT–Hierarchical Attention Network (HAN) framework for deobfuscating Iraqi Arabic leetspeak and accurately classifying hate speech. The proposed model employs a custom normalization pipeline that converts digits, symbols, and Latin-script substitutions (e.g., "3يب" → "عيب") into canonical Arabic forms, thereby enhancing tokenization and embedding quality. AraBERT provides deep contextualized representations optimized for Arabic morphology, while HAN hierarchically aggregates and attends to critical words and sentences to improve interpretability and semantic focus. Experimental evaluation on an Iraqi Arabic social media dataset demonstrates that the proposed model achieves 97% accuracy, 96% precision, 96% recall, 96% F1-score, and 0.98 ROC–AUC, outperforming standalone AraBERT and HAN models by up to 6% in F1-score and 4% in AUC. Ablation studies confirm the important role of the normalization stage (F1 = 0.91 without it) and the contribution of hierarchical attention in balancing precision and recall. Robustness testing under controlled perturbations (including character substitutions, symbol obfuscations, typographical noise, and class imbalance) shows performance retention above 91% F1, validating the framework’s noise tolerance and generalization capability. Comparative analysis with state-of-the-art approaches such as DRNNs, arHateDetector, and ensemble BERT systems further highlights the hybrid model’s effectiveness in handling noisy, dialectal, and adversarial text. Full article
(This article belongs to the Special Issue Natural Language Processing: From Deep Learning to Real-World Solutions)
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