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Natural Language Processing and Text Mining
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Natural Language Processing and Text Mining

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 December 2025 | Viewed by 2830

Special Issue Editor

Prof. Dr. John Atkinson Abutridy

E-Mail Website
Guest Editor
Faculty of Engineering and Sciences, Universidad Adolfo Ibañez (UAI), Santiago, Chile
Interests: natural language processing; text analytics; machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue on natural language processing (NLP) and text mining aims to explore the latest advancements and applications of the processing and analysis of natural language data aimed at producing novel knowledge for decision making. This issue welcomes contributions that focus on innovative algorithms, models, and techniques for understanding, interpreting, and generating human language. We encourage submissions on a wide range of topics, including, but not limited to, syntactic and semantic analysis, sentiment analysis, information extraction, machine translation, pattern discovery, and language modeling. Additionally, research on the integration of NLP with other technologies, such as machine learning, deep learning, and neural networks, is highly valued. We also seek papers that address practical applications of text mining in various domains, including healthcare, finance, social media, and cybersecurity. The goal is to provide a comprehensive overview of state-of-the-art NLP and text mining, highlighting both theoretical advances and practical implementations.

Prof. Dr. John Atkinson Abutridy
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. 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 (NLP)
  • text mining
  • sentiment analysis
  • information extraction
  • machine translation
  • language modeling
  • deep learning
  • neural networks
  • semantic analysis
  • syntactic analysis

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Published Papers (2 papers)

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Research

23 pages, 1604 KiB  
Article
Fine-Tuning Large Language Models for Kazakh Text Simplification
by Alymzhan Toleu, Gulmira Tolegen and Irina Ualiyeva
Appl. Sci. 2025, 15(15), 8344; https://doi.org/10.3390/app15158344 - 26 Jul 2025
Viewed by 554
Abstract
This paper addresses text simplification task for Kazakh, a morphologically rich, low-resource language, by introducing KazSim, an instruction-tuned model built on multilingual large language models (LLMs). First, we develop a heuristic pipeline to identify complex Kazakh sentences, manually validating its performance on 400 [...] Read more.
This paper addresses text simplification task for Kazakh, a morphologically rich, low-resource language, by introducing KazSim, an instruction-tuned model built on multilingual large language models (LLMs). First, we develop a heuristic pipeline to identify complex Kazakh sentences, manually validating its performance on 400 examples and comparing it against a purely LLM-based selection method; we then use this pipeline to assemble a parallel corpus of 8709 complex–simple pairs via LLM augmentation. For the simplification task, we benchmark KazSim against standard Seq2Seq systems, domain-adapted Kazakh LLMs, and zero-shot instruction-following models. On an automatically constructed test set, KazSim (Llama-3.3-70B) achieves BLEU 33.50, SARI 56.38, and F1 87.56 with a length ratio of 0.98, outperforming all baselines. We also explore prompt language (English vs. Kazakh) and conduct human evaluation with three native speakers: KazSim scores 4.08 for fluency, 4.09 for meaning preservation, and 4.42 for simplicity—significantly above GPT-4o-mini. Error analysis shows that remaining failures cluster into tone change, tense change, and semantic drift, reflecting Kazakh’s agglutinative morphology and flexible syntax. Full article
(This article belongs to the Special Issue Natural Language Processing and Text Mining)
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18 pages, 1646 KiB  
Article
An Entity-Relation Extraction Method Based on the Mixture-of-Experts Model and Dependency Parsing
by Yuanxi Li, Haiyan Wang and Dong Zhang
Appl. Sci. 2025, 15(4), 2119; https://doi.org/10.3390/app15042119 - 17 Feb 2025
Cited by 1 | Viewed by 1094
Abstract
Entity-relation extraction (ERE) aims to identify entity types and the relationships between them from unstructured texts and is one of the key technologies for constructing knowledge graphs. However, ERE tasks face challenges such as insufficient semantic representations and the complexity of relationship types, [...] Read more.
Entity-relation extraction (ERE) aims to identify entity types and the relationships between them from unstructured texts and is one of the key technologies for constructing knowledge graphs. However, ERE tasks face challenges such as insufficient semantic representations and the complexity of relationship types, which lead to the difficulty of triplet extraction. To address these issues, we propose an entity-relation extraction model that incorporates dependency parsing and a mixture-of-experts architecture. Specifically, we use BERT as a character encoder, while integrating dependency syntax information as a separate encoding path. We apply additive attention to fuse the two pathways of encoding, assigning different weights to each vector in the encoding layer output through a learned weighting process. This enables the model to flexibly adjust the attention given to different features, allowing for a more accurate identification and utilization of syntactic dependencies within a sentence. In the relation classification layer, we employ a mixture-of-experts architecture, allowing each expert to focus on learning different relationship labels, thereby enhancing the model’s ability to accurately identify and capture specific entity relationships. The proposed model achieves superior results to the baseline models on two public ERE datasets, providing a novel and effective solution for entity-relation extraction tasks. Full article
(This article belongs to the Special Issue Natural Language Processing and Text Mining)
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