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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: 20 November 2024 | Viewed by 2327

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 (2 papers)

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Research

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 946
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
Viewed by 904
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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