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Applied Sciences
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Practical Applications of Large Language Models in Natural Language Processing
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Practical Applications of Large Language Models in 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: 30 April 2026 | Viewed by 390

Special Issue Editors

Dr. Yongrui Chen

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
Interests: artificial intelligence; natural language processing; knowledge graph; large language model
Prof. Dr. Guilin Qi

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
Interests: natural language processing; knowledge graph; multimodal learning
Special Issues, Collections and Topics in MDPI journals
Dr. Hui Yang

E-Mail Website
Guest Editor Assistant
School of Engineering, University of Manchester, Manchester M13 9PL, UK
Interests: ontology embeddings; description logic

Special Issue Information

Dear Colleagues,

In the rapidly evolving landscape of artificial intelligence and computational linguistics, Large Language Models (LLMs) have emerged as transformative tools that are revolutionizing how we approach natural language processing tasks. The unprecedented capabilities of these models in understanding, generating, and manipulating human language have opened new frontiers in both theoretical research and practical applications across diverse domains. However, the successful deployment of LLMs in real-world scenarios presents unique challenges related to computational efficiency, domain adaptation, ethical considerations, and integration with existing systems. This Special Issue aims to bridge the gap between cutting-edge LLM research and practical implementation by showcasing innovative approaches that demonstrate the tangible benefits and solutions that these models provide to natural language processing challenges.

We invite the submission of original research contributions including, but not limited to, the following areas: LLM-powered textual analysis and understanding; domain-specific fine-tuning and adaptation; information extraction and knowledge graph construction; advanced conversational AI and personalized user experiences; applications in specialized sectors such as law, healthcare, and software engineering; efficient deployment strategies, including retrieval-augmented generation (RAG) and model compression; ethical AI, focusing on fairness, explainability, and bias mitigation; and the integration of LLMs with external tools and traditional NLP pipelines. Advancements in these areas promise to accelerate the adoption of intelligent language technologies, leading to more sophisticated and accessible natural language processing solutions for diverse industries and applications.

Dr. Yongrui Chen
Prof. Dr. Guilin Qi
Guest Editors

Dr. Hui Yang
Guest Editor Assistant

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-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

  • large language models
  • applied natural language processing
  • domain adaptation
  • efficient deployment
  • ethical AI

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Published Papers (1 paper)

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Research

17 pages, 1206 KB  
Article
DPATransLLM: Detection of Pronominal Anaphora in Turkish Sentences Using Transformer-Based, Large Language Models and Hybrid Ensemble Approach
by Engin Demir and Metin Bilgin
Appl. Sci. 2025, 15(23), 12480; https://doi.org/10.3390/app152312480 - 25 Nov 2025
Viewed by 187
Abstract
In the current information age, with the exponential growth of data volume and language-based applications, the accurate resolution of intra-contextual relationships in texts has become indispensable for both academic research and industrial Natural Language Processing (NLP) systems. This study focuses on the detection [...] Read more.
In the current information age, with the exponential growth of data volume and language-based applications, the accurate resolution of intra-contextual relationships in texts has become indispensable for both academic research and industrial Natural Language Processing (NLP) systems. This study focuses on the detection of pronominal anaphora in Turkish sentences. For the detection of pronominal anaphora, a specific dataset comprising 2000 sentences and 72,239 tokens was created, and this dataset was labeled using a BIO tagging method developed with a custom approach for this study. In this work, fine-tuning was performed on Transformer-based language models pre-trained on Turkish data, such as BERT and RoBERTa. Additionally, Large Language Models (LLMs) trained on Turkish data, including Turkcell-LLM-7b-v1 and ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1, as well as multilingual models like Microsoft’s Phi-3 Mini-4K-Instruct and OpenAI’s GPT-4o-mini, were also fine-tuned with the created dataset to detect pronominal anaphora in sentences. Following the training of the language models, the resulting performance was evaluated using pronoun accuracy, antecedent accuracy, exact match, and F1-score metrics. According to the results obtained in the pronominal anaphora detection phase of the study, a novel hybrid ensemble approach combining multiple Transformer models with linguistic rules achieved the highest performance. This hybrid system attained scores of 0.987 for pronoun accuracy, 0.977 for antecedent accuracy, 0.505 for exact match, and 0.960 for F1-score, surpassing all individual models, including GPT-4o-mini. These findings reveal the superiority of ensemble methods combined with Turkish-specific linguistic rules over standalone models in Turkish anaphora resolution. This study is considered novel, as it is the first work to apply hybrid ensemble methods with linguistic rule integration to this domain for the Turkish language. Full article
(This article belongs to the Special Issue Practical Applications of Large Language Models in Natural Language Processing)
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