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New Perspectives in Natural Language Processing and Computational Linguistics

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

Deadline for manuscript submissions: closed (20 February 2026) | Viewed by 5431

Special Issue Editor


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Guest Editor
Department of Applied Economics (Quantitative Methods), Faculty of Economics, University of Valencia, Av/Tarongers s/n, 46022 Valencia, Spain
Interests: natural language processing; computational linguistics

Special Issue Information

Dear Colleagues,

The fields of natural language processing (NLP) and computational linguistics (CL) have undergone dramatic transformations in recent years and the ongoing development of new methodologies, algorithms, and data-driven approaches is continuously reshaping the ways machines understand, process, and generate human language.

The goal of this Special Issue attract innovative ideas and emerging trends as well as interdisciplinary approaches that push the boundaries of what is possible in these fields.  New perspectives in NLP and CL highlight a shift towards more powerful, inclusive, and ethical AI systems that are not only capable of understanding language but can also reason, explain, and interact with the world in increasinlgly sophisticated ways. As NLP continues to evolve, its impact on industries ranging from healthcare to entertainment, education, and beyond will continue to grow, transforming how we interact with machines and how machines understand and process human language.

Topics relevant to this SI include, but are not limited to:

  • Deep learning and transformer architectures;
  • Pretrained language models;
  • Multilingual and cross-lingual NLP;
  • Explainability and interpretability;
  • Bias and fairness in NLP;
  • Ethical considerations and societal impact;
  • Multimodal NLP;
  • Low-resource and zero-shot learning;
  • Human-in-the-loop NLP;
  • Integrating knowledge and reasoning;
  • Analytical, numerical, and computational analysis;
  • Structural dynamics;
  • Experimental investigations.

This dynamic and rapidly developing field offers exciting opportunities for innovation, collaboration, and addressing real-world challenges through the power of language technologies.

Prof. Dr. Víctor Fernández
Guest Editor

Manuscript Submission Information

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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

  • interoperabiity
  • transformers
  • pretrained models
  • multilingual NLP
  • bias and fairness
  • explainability
  • reasoning
  • generative models
  • few-shot learning
  • multimodal learning
  • smartcity
  • AI

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Published Papers (2 papers)

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Research

21 pages, 1574 KB  
Article
Turkish Telephone Conversations in Credit Risk Management: Natural Language Processing and LSTM Approach
by Emre Ridvan Muratlar, Dogan Yildiz and Erhan Ustaoglu
Appl. Sci. 2026, 16(1), 108; https://doi.org/10.3390/app16010108 - 22 Dec 2025
Viewed by 557
Abstract
This study aims to analyze text data obtained from Turkish phone calls to manage credit risk in the banking sector and predict whether customers will fulfill their payment promises. Data cleaning was identified as a critical step to improve the quality of the [...] Read more.
This study aims to analyze text data obtained from Turkish phone calls to manage credit risk in the banking sector and predict whether customers will fulfill their payment promises. Data cleaning was identified as a critical step to improve the quality of the texts, and various natural language processing (NLP) techniques were used. The model was built using a two-layer LSTM architecture, starting with a Self-Embedding layer, and achieved approximately 80% accuracy on the test data. The findings indicate that customers who break their payment promises often cite personal life issues such as health problems, family issues, financial difficulties, and religious beliefs to ensure reliability. These results demonstrate the importance of text data in the banking sector, the applicability of different embedding methods to Turkish datasets, and their advantages and disadvantages. Furthermore, the model built using data obtained from customer conversations can help predict credit risk more accurately and contribute to improving call center processes. Automating data cleaning processes and developing speech-to-text translation tools are recommended for future studies. Full article
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24 pages, 5192 KB  
Article
Cross-Lingual Summarization for Low-Resource Languages Using Multilingual Retrieval-Based In-Context Learning
by Gyutae Park, Jeonghyun Park and Hwanhee Lee
Appl. Sci. 2025, 15(14), 7800; https://doi.org/10.3390/app15147800 - 11 Jul 2025
Cited by 5 | Viewed by 4327
Abstract
Cross-lingual summarization (XLS) involves generating a summary in one language from an article written in another language. XLS presents substantial hurdles due to the complex linguistic structures across languages and the challenges in transferring knowledge effectively between them. Although Large Language Models (LLMs) [...] Read more.
Cross-lingual summarization (XLS) involves generating a summary in one language from an article written in another language. XLS presents substantial hurdles due to the complex linguistic structures across languages and the challenges in transferring knowledge effectively between them. Although Large Language Models (LLMs) have demonstrated capabilities in cross-lingual tasks, the integration of retrieval-based in-context learning remains largely unexplored, despite its potential to overcome these linguistic barriers by providing relevant examples. In this paper, we introduce Multilingual Retrieval-based Cross-lingual Summarization (MuRXLS), a robust framework that dynamically selects the most relevant summarization examples for each article using multilingual retrieval. Our method leverages multilingual embedding models to identify contextually appropriate demonstrations for various LLMs. Experiments across twelve XLS setups (six language pairs in both directions) reveal a notable directional asymmetry: our approach significantly outperforms baselines in many-to-one (X→English) scenarios, while showing comparable performance in one-to-many (English→X) directions. We also observe a strong correlation between article-example semantic similarity and summarization quality, demonstrating that intelligently selecting contextually relevant examples substantially improves XLS performance by providing LLMs with more informative demonstrations. Full article
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