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New Trends in Natural Language Processing
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New Trends 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 June 2025 | Viewed by 8006

Special Issue Editors

Dr. Alaa Mohasseb

E-Mail Website
Guest Editor
School of Computing, University of Portsmouth, Portsmouth PO1 3HE, UK
Interests: artificial intelligence; natural language processing; text mining; machine learning; deep learning; information retrieval
Special Issues, Collections and Topics in MDPI journals
Dr. Andreas Kanavos

E-Mail Website
Guest Editor
Department of Informatics, Ionian University, Corfu, Greece
Interests: artificial intelligence; machine learning; data management and mining; big data; social network analysis; information retrieval
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advancements that have been made regarding algorithms, data availability, methodologies, linguistic models, and the structural frameworks of computational linguistics are significant forces which are driving the field of Natural Language Processing (NLP) forwards. These developments have introduced a range of new challenges and opportunities into the conceptualisation and implementation of NLP systems, covering both theoretical frameworks and practical applications; this Special Issue focuses on highlighting innovative research and experimental outcomes in the study of NLP, extending from core language models and machine learning strategies to their application in real-world scenarios.

The Special Issue invites original, high-quality research contributions that traverse various domains within NLP research, including but not limited to:

  • Machine learning and deep learning for semantic analysis and language understanding.
  • Text analytics, Classification and Extraction.
  • Sentiment analysis, and summarization techniques.
  • Speech Processing and Recognition.
  • Big Data Methods for Computational Linguistics.
  • Semantic technologies, language ontologies, and natural language understanding.
  • Emerging trends in computational linguistics and language models.

Dr. Alaa Mohasseb
Dr. Andreas Kanavos
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 100 words) can be sent to the Editorial Office for announcement on this website.

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 processing
  • deep learning in NLP
  • semantic analysis
  • language models
  • sentiment analysis
  • text classification
  • text summarization
  • multilingual NLP
  • low-resource language processing
  • NLP applications

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Published Papers (4 papers)

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Research

24 pages, 1610 KiB  
Article
Leveraging Advanced NLP Techniques and Data Augmentation to Enhance Online Misogyny Detection
by Alaa Mohasseb, Eslam Amer, Fatima Chiroma and Alessia Tranchese
Appl. Sci. 2025, 15(2), 856; https://doi.org/10.3390/app15020856 - 16 Jan 2025
Viewed by 683
Abstract
Online misogyny is a significant societal challenge that reinforces gender inequalities and discourages women from engaging fully in digital spaces. Traditional moderation methods often fail to address the dynamic and context-dependent nature of misogynistic language, making adaptive solutions essential. This study presents a [...] Read more.
Online misogyny is a significant societal challenge that reinforces gender inequalities and discourages women from engaging fully in digital spaces. Traditional moderation methods often fail to address the dynamic and context-dependent nature of misogynistic language, making adaptive solutions essential. This study presents a framework that integrates advanced natural-language processing techniques with strategic data augmentation to improve the detection of misogynistic content. Key contributions include emoji decoding to interpret symbolic communication, contextual expansion using Sentence-Transformer models, and LDA-based topic modeling to enhance data richness and contextual understanding. The framework incorporates machine-learning, deep-learning, and Transformer-based models to handle complex and nuanced language. Performance analysis highlights the effectiveness of the selected models, and comparative results emphasize the transformative role of data augmentation. This augmentation significantly enhanced model robustness, improved generalization, and strengthened the detection of misogynistic content. Full article
(This article belongs to the Special Issue New Trends in Natural Language Processing)
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26 pages, 1463 KiB  
Article
Natural Language Processing Tools and Workflows for Improving Research Processes
by Noel Khan, David Elizondo, Lipika Deka and Miguel A. Molina-Cabello
Appl. Sci. 2024, 14(24), 11731; https://doi.org/10.3390/app142411731 - 16 Dec 2024
Viewed by 888
Abstract
The modern research process involves refining a set of keywords until sufficiently pertinent results are obtained from acceptable sources. References and citations from the most relevant results can then be traced to related works. This process iteratively develops a set of keywords to [...] Read more.
The modern research process involves refining a set of keywords until sufficiently pertinent results are obtained from acceptable sources. References and citations from the most relevant results can then be traced to related works. This process iteratively develops a set of keywords to find the most relevant literature. However, because a keyword-based search essentially samples a corpus, it may be inadequate for capturing a broad or exhaustive understanding of a topic. Further, a keyword-based search is dependent upon the underlying storage and retrieval technology and is essentially a syntactical search rather than a semantic search. To overcome such limitations, this paper explores the use of well-known natural language processing (NLP) techniques to support a semantic search and identifies where specific NLP techniques can be employed and what their primary benefits are, thus enhancing the opportunities to further improve the research process. The proposed NLP methods were tested through different workflows on different datasets and each workflow was designed to exploit latent relationships within the data to refine the keywords. The results of these tests demonstrated an improvement in the identified literature when compared to the literature extracted from the end-user-given keywords. For example, one of the defined workflows reduced the number of search results by two orders of magnitude but contained a larger percentage of pertinent results. Full article
(This article belongs to the Special Issue New Trends in Natural Language Processing)
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17 pages, 2571 KiB  
Article
Enhancing E-Government Services through State-of-the-Art, Modular, and Reproducible Architecture over Large Language Models
by George Papageorgiou, Vangelis Sarlis, Manolis Maragoudakis and Christos Tjortjis
Appl. Sci. 2024, 14(18), 8259; https://doi.org/10.3390/app14188259 - 13 Sep 2024
Viewed by 3626
Abstract
Integrating Large Language Models (LLMs) into e-government applications has the potential to improve public service delivery through advanced data processing and automation. This paper explores critical aspects of a modular and reproducible architecture based on Retrieval-Augmented Generation (RAG) for deploying LLM-based assistants within [...] Read more.
Integrating Large Language Models (LLMs) into e-government applications has the potential to improve public service delivery through advanced data processing and automation. This paper explores critical aspects of a modular and reproducible architecture based on Retrieval-Augmented Generation (RAG) for deploying LLM-based assistants within e-government systems. By examining current practices and challenges, we propose a framework ensuring that Artificial Intelligence (AI) systems are modular and reproducible, essential for maintaining scalability, transparency, and ethical standards. Our approach utilizing Haystack demonstrates a complete multi-agent Generative AI (GAI) virtual assistant that facilitates scalability and reproducibility by allowing individual components to be independently scaled. This research focuses on a comprehensive review of the existing literature and presents case study examples to demonstrate how such an architecture can enhance public service operations. This framework provides a valuable case study for researchers, policymakers, and practitioners interested in exploring the integration of advanced computational linguistics and LLMs into e-government services, although it could benefit from further empirical validation. Full article
(This article belongs to the Special Issue New Trends in Natural Language Processing)
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14 pages, 2006 KiB  
Article
MicroBERT: Distilling MoE-Based Knowledge from BERT into a Lighter Model
by Dashun Zheng, Jiaxuan Li, Yunchu Yang, Yapeng Wang and Patrick Cheong-Iao Pang
Appl. Sci. 2024, 14(14), 6171; https://doi.org/10.3390/app14146171 - 16 Jul 2024
Cited by 3 | Viewed by 1937
Abstract
Natural language-processing tasks have been improved greatly by large language models (LLMs). However, numerous parameters make their execution computationally expensive and difficult on resource-constrained devices. For this problem, as well as maintaining accuracy, some techniques such as distillation and quantization have been proposed. [...] Read more.
Natural language-processing tasks have been improved greatly by large language models (LLMs). However, numerous parameters make their execution computationally expensive and difficult on resource-constrained devices. For this problem, as well as maintaining accuracy, some techniques such as distillation and quantization have been proposed. Unfortunately, current methods fail to integrate model pruning with downstream tasks and overlook sentence-level semantic modeling, resulting in reduced efficiency of distillation. To alleviate these limitations, we propose a novel distilled lightweight model for BERT named MicroBERT. This method can transfer the knowledge contained in the “teacher” BERT model to a “student” BERT model. The sentence-level feature alignment loss (FAL) distillation mechanism, guided by Mixture-of-Experts (MoE), captures comprehensive contextual semantic knowledge from the “teacher” model to enhance the “student” model’s performance while reducing its parameters. To make the outputs of “teacher” and “student” models comparable, we introduce the idea of a generative adversarial network (GAN) to train a discriminator. Our experimental results based on four datasets show that all steps of our distillation mechanism are effective, and the MicroBERT (101.14%) model outperforms TinyBERT (99%) by 2.24% in terms of average distillation reductions in various tasks on the GLUE dataset. Full article
(This article belongs to the Special Issue New Trends in Natural Language Processing)
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