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Natural Language Processing (NLP) and Large Language Modelling (2nd Edition)
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Natural Language Processing (NLP) and Large Language Modelling (2nd Edition)

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "AI-Driven Innovations".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 3797

Special Issue Editor

Dr. Ming Liu

E-Mail Website
Guest Editor
School of Info Technology, Faculty of Science, Engineering and Built Environment, Geelong Waurn Ponds Campus, Deakin University, Geelong, VIC 3216, Australia
Interests: natural language processing; small efficient language modelling; continual learning; text generation; adversarial learning; scientific text mining; multimodality; conversational systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Natural Language Processing is a rapidly evolving field playing a crucial role in shaping the future of human–computer interactions, with applications range from sentiment analysis and machine translation to question-answering and dialogue systems.

We invite researchers, practitioners, and enthusiasts to submit original research articles, reviews, and case studies that contribute to the advancement of NLP. We also welcome extended conference papers that comprise at least 50% of original material, e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases. Topics of interest for this Special Issue include, but are not limited to, the following:

  • Large language modelling and its applications;
  • Sentiment analysis and opinion mining;
  • Machine translation and multilingual processing;
  • Question-answering and information retrieval;
  • Dialogue systems and conversational agents;
  • Text summarization and generation;
  • Natural language understanding and generation;
  • NLP applications in healthcare, finance, education, and other domains.

Submissions should present novel research findings, innovative methodologies, and practical applications that demonstrate the current state of the art in NLP. We welcome interdisciplinary approaches and encourage submissions that explore the intersection of NLP and other fields, such as machine learning, artificial intelligence, and cognitive science.

Dr. Ming Liu
Guest Editor

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. Computers is an international peer-reviewed open access monthly 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 1800 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
  • small efficient language modelling
  • continual learning
  • text generation
  • adversarial learning
  • scientific text mining
  • multimodality
  • conversational systems
  • large language model

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Related Special Issue

Published Papers (4 papers)

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Research

47 pages, 3137 KB  
Article
DietQA: A Comprehensive Framework for Personalized Multi-Diet Recipe Retrieval Using Knowledge Graphs, Retrieval-Augmented Generation, and Large Language Models
by Ioannis Tsampos and Emmanouil Marakakis
Computers 2025, 14(10), 412; https://doi.org/10.3390/computers14100412 - 29 Sep 2025
Viewed by 680
Abstract
Recipes available on the web often lack nutritional transparency and clear indicators of dietary suitability. While searching by title is straightforward, exploring recipes that meet combined dietary needs, nutritional goals, and ingredient-level preferences remains challenging. Most existing recipe search systems do not effectively [...] Read more.
Recipes available on the web often lack nutritional transparency and clear indicators of dietary suitability. While searching by title is straightforward, exploring recipes that meet combined dietary needs, nutritional goals, and ingredient-level preferences remains challenging. Most existing recipe search systems do not effectively support flexible multi-dietary reasoning in combination with user preferences and restrictions. For example, users may seek gluten-free and dairy-free dinners with suitable substitutions, or compound goals such as vegan and low-fat desserts. Recent systematic reviews report that most food recommender systems are content-based and often non-personalized, with limited support for dietary restrictions, ingredient-level exclusions, and multi-criteria nutrition goals. This paper introduces DietQA, an end-to-end, language-adaptable chatbot system that integrates a Knowledge Graph (KG), Retrieval-Augmented Generation (RAG), and a Large Language Model (LLM) to support personalized, dietary-aware recipe search and question answering. DietQA crawls Greek-language recipe websites to extract structured information such as titles, ingredients, and quantities. Nutritional values are calculated using validated food composition databases, and dietary tags are inferred automatically based on ingredient composition. All information is stored in a Neo4j-based knowledge graph, enabling flexible querying via Cypher. Users interact with the system through a natural language chatbot friendly interface, where they can express preferences for ingredients, nutrients, dishes, and diets, and filter recipes based on multiple factors such as ingredient availability, exclusions, and nutritional goals. DietQA supports multi-diet recipe search by retrieving both compliant recipes and those adaptable via ingredient substitutions, explaining how each result aligns with user preferences and constraints. An LLM extracts intents and entities from user queries to support rule-based Cypher retrieval, while the RAG pipeline generates contextualized responses using the user query and preferences, retrieved recipes, statistical summaries, and substitution logic. The system integrates real-time updates of recipe and nutritional data, supporting up-to-date, relevant, and personalized recommendations. It is designed for language-adaptable deployment and has been developed and evaluated using Greek-language content. DietQA provides a scalable framework for transparent and adaptive dietary recommendation systems powered by conversational AI. Full article
(This article belongs to the Special Issue Natural Language Processing (NLP) and Large Language Modelling (2nd Edition))
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32 pages, 852 KB  
Article
Benchmarking the Responsiveness of Open-Source Text-to-Speech Systems
by Ha Pham Thien Dinh, Rutherford Agbeshi Patamia, Ming Liu and Akansel Cosgun
Computers 2025, 14(10), 406; https://doi.org/10.3390/computers14100406 - 23 Sep 2025
Viewed by 1351
Abstract
Responsiveness—the speed at which a text-to-speech (TTS) system produces audible output—is critical for real-time voice assistants yet has received far less attention than perceptual quality metrics. Existing evaluations often touch on latency but do not establish reproducible, open-source standards that capture responsiveness as [...] Read more.
Responsiveness—the speed at which a text-to-speech (TTS) system produces audible output—is critical for real-time voice assistants yet has received far less attention than perceptual quality metrics. Existing evaluations often touch on latency but do not establish reproducible, open-source standards that capture responsiveness as a first-class dimension. This work introduces a baseline benchmark designed to fill that gap. Our framework unifies latency distribution, tail latency, and intelligibility within a transparent and dataset-diverse pipeline, enabling a fair and replicable comparison across 13 widely used open-source TTS models. By grounding evaluation in structured input sets ranging from single words to sentence-length utterances and adopting a methodology inspired by standardized inference benchmarks, we capture both typical and worst-case user experiences. Unlike prior studies that emphasize closed or proprietary systems, our focus is on establishing open, reproducible baselines rather than ranking against commercial references. The results reveal substantial variability across architectures, with some models delivering near-instant responses while others fail to meet interactive thresholds. By centering evaluation on responsiveness and reproducibility, this study provides an infrastructural foundation for benchmarking TTS systems and lays the groundwork for more comprehensive assessments that integrate both fidelity and speed. Full article
(This article belongs to the Special Issue Natural Language Processing (NLP) and Large Language Modelling (2nd Edition))
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21 pages, 356 KB  
Article
Integrating Large Language Models with near Real-Time Web Crawling for Enhanced Job Recommendation Systems
by David Gauhl, Kevin Kakkanattu, Melbin Mukkattu and Thomas Hanne
Computers 2025, 14(9), 387; https://doi.org/10.3390/computers14090387 - 15 Sep 2025
Viewed by 663
Abstract
This study addresses the limitations of traditional job recommendation systems that rely on static datasets, making them less responsive to dynamic job market changes. While existing job platforms address job search with an untransparent logic following their business goals, job seekers may benefit [...] Read more.
This study addresses the limitations of traditional job recommendation systems that rely on static datasets, making them less responsive to dynamic job market changes. While existing job platforms address job search with an untransparent logic following their business goals, job seekers may benefit from a solution actively and dynamically crawling and evaluating job offers from a variety of sites according to their objectives. To address this gap, a hybrid system was developed that integrates large language models (LLMs) for semantic analysis with near real-time data acquisition through web crawling. The system extracts and ranks job-specific keywords from user inputs, such as resumes, while dynamically retrieving job listings from online platforms. User evaluations indicated strong performance in keyword extraction and system usability but revealed challenges in web crawler performance, affecting recommendation accuracy. Compared with a state-of-the-art commercial tool, user tests indicate a smaller accuracy of our prototype but a higher functionality satisfaction. Test users highlighted its great potential for further development. The results highlight the benefits of combining LLMs and web crawling while emphasizing the need for improved near real-time data handling to enhance recommendation precision and user satisfaction. Full article
(This article belongs to the Special Issue Natural Language Processing (NLP) and Large Language Modelling (2nd Edition))
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18 pages, 4208 KB  
Article
Transformer Models for Paraphrase Detection: A Comprehensive Semantic Similarity Study
by Dianeliz Ortiz Martes, Evan Gunderson, Caitlin Neuman and Nezamoddin N. Kachouie
Computers 2025, 14(9), 385; https://doi.org/10.3390/computers14090385 - 14 Sep 2025
Viewed by 819
Abstract
Semantic similarity, the task of determining whether two sentences convey the same meaning, is central to applications such as paraphrase detection, semantic search, and question answering. Despite the widespread adoption of transformer-based models for this task, their performance is influenced by both the [...] Read more.
Semantic similarity, the task of determining whether two sentences convey the same meaning, is central to applications such as paraphrase detection, semantic search, and question answering. Despite the widespread adoption of transformer-based models for this task, their performance is influenced by both the choice of similarity measure and BERT (bert-base-nli-mean-tokens), RoBERTa (all-roberta-large-v1), and MPNet (all-mpnet-base-v2) on the Microsoft Research Paraphrase Corpus (MRPC). Sentence embeddings were compared using cosine similarity, dot product, Manhattan distance, and Euclidean distance, with thresholds optimized for accuracy, balanced accuracy, and F1-score. Results indicate a consistent advantage for MPNet, which achieved the highest accuracy (75.6%), balanced accuracy (71.0%), and F1-score (0.836) when paired with cosine similarity at an optimized threshold of 0.671. BERT and RoBERTa performed competitively but exhibited greater sensitivity to the choice of Similarity metric, with BERT notably underperforming when using cosine similarity compared to Manhattan or Euclidean distance. Optimal thresholds varied widely (0.334–0.867), underscoring the difficulty of establishing a single, generalizable cut-off for paraphrase classification. These findings highlight the value of fine-tuning of both Similarity metrics and thresholds alongside model selection, offering practical guidance for designing high-accuracy semantic similarity systems in real-world NLP applications. Full article
(This article belongs to the Special Issue Natural Language Processing (NLP) and Large Language Modelling (2nd Edition))
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