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Techniques and Applications of Natural Language Processing
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Techniques and Applications of Natural Language Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 October 2025 | Viewed by 7832

Special Issue Editors

Dr. Rajvardhan Patil

E-Mail Website
Guest Editor
School of Computing, Grand Valley State University, Allendale Charter Township, MI 49401, USA
Interests: natural language processing; retrieval augmented generation; prompt engineering
Prof. Dr. Venkat N. Gudivada

E-Mail Website
Guest Editor
Department of Computer Science, East Carolina University, Greenville, NC 27858-4353, USA
Interests: data management; high-performance computing; information retrieval; natural language processing; cognitive computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent developments in large language models have revolutionized the field of natural language processing, resulting in a wide range of applications such as chatbots, content generation, language translation, sentiment analysis, question-answering systems, and personalized recommendations. However, several problems such as scalability, hallucination, biases, privacy, ethical issues, fairness, the effective tokenization of low-resource languages, multilingual capabilities, and efficient fine-tuning techniques of models demand further attention. This Special Issue aims to address the aforementioned challenges by inviting scholarly contributions that advance processing techniques and applications in the field of NLP. We welcome original research articles, best practice papers, and review papers that report the development of NLP models, algorithms, and applications.

Dr. Rajvardhan Patil
Prof. Dr. Venkat N. Gudivada
Guest Editors

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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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences 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

  • natural language understanding (NLU)
  • natural language generation (NLG)
  • large language models (LLMs)
  • transfer learning
  • fine tuning
  • in-context learning
  • prompt engineering
  • knowledge graph
  • vector databases
  • retrieval augmented generation (RAG)
  • task-oriented NLP applications (such as knowledge extraction, question answering, and sentiment analysis)
  • NLP applications in specific domains (such as life sciences, health, and medicine)

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Published Papers (5 papers)

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Research

20 pages, 1325 KiB  
Article
Does the Grammatical Structure of Prompts Influence the Responses of Generative Artificial Intelligence? An Exploratory Analysis in Spanish
by Rhoddy Viveros-Muñoz, José Carrasco-Sáez, Carolina Contreras-Saavedra, Sheny San-Martín-Quiroga and Carla E. Contreras-Saavedra
Appl. Sci. 2025, 15(7), 3882; https://doi.org/10.3390/app15073882 - 2 Apr 2025
Viewed by 1235
Abstract
Generative Artificial Intelligence (AI) has transformed personal and professional domains by enabling creative content generation and problem-solving. However, the influence of users’ grammatical abilities on AI-generated responses remains unclear. This exploratory study examines how language and grammar abilities in Spanish affect the quality [...] Read more.
Generative Artificial Intelligence (AI) has transformed personal and professional domains by enabling creative content generation and problem-solving. However, the influence of users’ grammatical abilities on AI-generated responses remains unclear. This exploratory study examines how language and grammar abilities in Spanish affect the quality of responses from ChatGPT (version 3.5). Despite the robust performance of Large Language Models (LLMs) in various tasks, they face challenges with grammatical moods specific to non-English languages, such as the subjunctive in Spanish. Higher education students were chosen as participants due to their familiarity with AI and its potential use in learning. The study assessed ChatGPT’s ability to process instructions in Chilean Spanish, analyzing how linguistic complexity, grammatical variations, and informal language impacted output quality. The results indicate that varied verbal moods and complex sentence structures significantly influence prompt evaluation, response quality, and response length. Based on these findings, a framework is proposed to guide higher education communities in promoting digital literacy and integrating AI into teaching and learning. Full article
(This article belongs to the Special Issue Techniques and Applications of Natural Language Processing)
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22 pages, 669 KiB  
Article
Analyzing LLAMA3 Performance on Classification Task Using LoRA and QLoRA Techniques
by Rajvardhan Patil, Priyanka Khot and Venkat Gudivada
Appl. Sci. 2025, 15(6), 3087; https://doi.org/10.3390/app15063087 - 12 Mar 2025
Viewed by 1317
Abstract
Large language models (LLMs), consisting of billions and trillions of parameters, have demonstrated exceptional ability in natural language understanding (NLU) and natural language generation (NLG) tasks. Increases in their numbers of parameters and model sizes have resulted in better performance and accuracy. However, [...] Read more.
Large language models (LLMs), consisting of billions and trillions of parameters, have demonstrated exceptional ability in natural language understanding (NLU) and natural language generation (NLG) tasks. Increases in their numbers of parameters and model sizes have resulted in better performance and accuracy. However, models with such enormous numbers of parameters incur significant computational costs and resources, making them challenging to fine tune and adapt to a specific downstream task. Several parameter-efficient fine-tuning (PEFT) techniques have been proposed to address this issue. This study demonstrates the improvement obtained over the base LLaMA3-8B model using two prominent PEFT techniques: LoRA and QLoRA. We use the sequence classification task of sentiment analysis to conduct the experiments. Additionally, we analyze the effects of hyperparameter adjustments (r and α) on the model’s performance. We examine the tradeoff between efficiency and memory savings obtained using the quantized LoRA (QLoRA) technique. We also investigate and compare the performance changes of LoRA and QLoRA techniques obtained after adapting to attention layers (query, key, value, and project) to all the linear layers during fine tuning. We report the findings of our work along with limitations and future directions. Full article
(This article belongs to the Special Issue Techniques and Applications of Natural Language Processing)
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16 pages, 12177 KiB  
Article
An Advanced Natural Language Processing Framework for Arabic Named Entity Recognition: A Novel Approach to Handling Morphological Richness and Nested Entities
by Saleh Albahli
Appl. Sci. 2025, 15(6), 3073; https://doi.org/10.3390/app15063073 - 12 Mar 2025
Viewed by 555
Abstract
Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP) that supports applications such as information retrieval, sentiment analysis, and text summarization. While substantial progress has been made in NER for widely studied languages like English, Arabic presents unique challenges [...] Read more.
Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP) that supports applications such as information retrieval, sentiment analysis, and text summarization. While substantial progress has been made in NER for widely studied languages like English, Arabic presents unique challenges due to its morphological richness, orthographic ambiguity, and the frequent occurrence of nested and overlapping entities. This paper introduces a novel Arabic NER framework that addresses these complexities through architectural innovations. The proposed model incorporates a Hybrid Feature Fusion Layer, which integrates external lexical features using a cross-attention mechanism and a Gated Lexical Unit (GLU) to filter noise, while a Compound Span Representation Layer employs Rotary Positional Encoding (RoPE) and Bidirectional GRUs to enhance the detection of complex entity structures. Additionally, an Enhanced Multi-Label Classification Layer improves the disambiguation of overlapping spans and assigns multiple entity types where applicable. The model is evaluated on three benchmark datasets—ANERcorp, ACE 2005, and a custom biomedical dataset—achieving an F1-score of 93.0% on ANERcorp and 89.6% on ACE 2005, significantly outperforming state-of-the-art methods. A case study further highlights the model’s real-world applicability in handling compound and nested entities with high confidence. By establishing a new benchmark for Arabic NER, this work provides a robust foundation for advancing NLP research in morphologically rich languages. Full article
(This article belongs to the Special Issue Techniques and Applications of Natural Language Processing)
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14 pages, 1553 KiB  
Article
A Study on Performance Enhancement by Integrating Neural Topic Attention with Transformer-Based Language Model
by Taehum Um and Namhyoung Kim
Appl. Sci. 2024, 14(17), 7898; https://doi.org/10.3390/app14177898 - 5 Sep 2024
Cited by 1 | Viewed by 1445
Abstract
As an extension of the transformer architecture, the BERT model has introduced a new paradigm for natural language processing, achieving impressive results in various downstream tasks. However, high-performance BERT-based models—such as ELECTRA, ALBERT, and RoBERTa—suffer from limitations such as poor continuous learning capability [...] Read more.
As an extension of the transformer architecture, the BERT model has introduced a new paradigm for natural language processing, achieving impressive results in various downstream tasks. However, high-performance BERT-based models—such as ELECTRA, ALBERT, and RoBERTa—suffer from limitations such as poor continuous learning capability and insufficient understanding of domain-specific documents. To address these issues, we propose the use of an attention mechanism to combine BERT-based models with neural topic models. Unlike traditional stochastic topic modeling, neural topic modeling employs artificial neural networks to learn topic representations. Furthermore, neural topic models can be integrated with other neural models and trained to identify latent variables in documents, thereby enabling BERT-based models to sufficiently comprehend the contexts of specific fields. We conducted experiments on three datasets—Movie Review Dataset (MRD), 20Newsgroups, and YELP—to evaluate our model’s performance. Compared to the vanilla model, the proposed model achieved an accuracy improvement of 1–2% for the ALBERT model in multiclassification tasks across all three datasets, while the ELECTRA model showed an accuracy improvement of less than 1%. Full article
(This article belongs to the Special Issue Techniques and Applications of Natural Language Processing)
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17 pages, 718 KiB  
Article
MédicoBERT: A Medical Language Model for Spanish Natural Language Processing Tasks with a Question-Answering Application Using Hyperparameter Optimization
by Josué Padilla Cuevas, José A. Reyes-Ortiz, Alma D. Cuevas-Rasgado, Román A. Mora-Gutiérrez and Maricela Bravo
Appl. Sci. 2024, 14(16), 7031; https://doi.org/10.3390/app14167031 - 10 Aug 2024
Cited by 1 | Viewed by 2533
Abstract
The increasing volume of medical information available in digital format presents a significant challenge for researchers seeking to extract relevant information. Manually analyzing voluminous data is a time-consuming process that constrains researchers’ productivity. In this context, innovative and intelligent computational approaches to information [...] Read more.
The increasing volume of medical information available in digital format presents a significant challenge for researchers seeking to extract relevant information. Manually analyzing voluminous data is a time-consuming process that constrains researchers’ productivity. In this context, innovative and intelligent computational approaches to information search, such as large language models (LLMs), offer a promising solution. LLMs understand natural language questions and respond accurately to complex queries, even in the specialized domain of medicine. This paper presents MédicoBERT, a medical language model in Spanish developed by adapting a general domain language model (BERT) to medical terminology and vocabulary related to diseases, treatments, symptoms, and medications. The model was pre-trained with 3 M medical texts containing 1.1 B words. Furthermore, with promising results, MédicoBERT was adapted and evaluated to answer medical questions in Spanish. The question-answering (QA) task was fine-tuned using a Spanish corpus of over 34,000 medical questions and answers. A search was then conducted to identify the optimal hyperparameter configuration using heuristic methods and nonlinear regression models. The evaluation of MédicoBERT was carried out using metrics such as perplexity to measure the adaptation of the language model to the medical vocabulary in Spanish, where it obtained a value of 4.28, and the average F1 metric for the task of answering medical questions, where it obtained a value of 62.35%. The objective of MédicoBERT is to provide support for research in the field of natural language processing (NLP) in Spanish, with a particular emphasis on applications within the medical domain. Full article
(This article belongs to the Special Issue Techniques and Applications of Natural Language Processing)
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