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Applied Sciences
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Text Mining with Information Extraction: Latest Advances and Prospects
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Text Mining with Information Extraction: Latest Advances and Prospects

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 July 2026 | Viewed by 3672

Editors

Dr. Loukas Ilias

E-Mail Website
Guest Editor
Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Iroon Polytechniou 9, 15780 Athens, Greece
Interests: social media analysis; AI and health; biomedical text mining; computational social science; text mining
Dr. Dimitrios Askounis

E-Mail Website
Guest Editor
Division of Industrial Electric Devices and Decision Systems, National Technical University of Athens, Athens, Greece
Interests: decision support systems; e-government; interoperability; cybersecurity
Dr. Spiros Mouzakitis

E-Mail Website
Guest Editor
Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Iroon Polytechniou 9, 15780 Athens, Greece
Interests: artificial intelligence; big data; analytics; decision support systems; data engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the era of data deluge, text mining and information extraction have become critical tools for transforming unstructured textual data into actionable knowledge. With the rapid evolution of machine learning, natural language processing (NLP), and large language models (LLMs), the synergy between text mining and information extraction has entered a transformative phase.

This Special Issue aims to showcase cutting-edge research, methodologies, and applications at the intersection of text mining and information extraction. We invite contributions that explore innovative approaches, theoretical foundations, and practical implementations that push the boundaries of what is possible in this fast-evolving field.

From large-scale scientific corpora and biomedical studies to social media, legal documents, and news articles, text mining with information extraction is proving essential across numerous real-world applications. These technologies are driving innovation in both academic research and industry by enabling the discovery of hidden patterns, the automation of knowledge curation, and the extraction of actionable insights from complex and noisy textual data. This Special Issue encourages submissions that explore diverse, domain-specific use cases, as well as those that address the methodological and computational challenges involved in scaling and adapting information extraction systems across various contexts.

Topics of interest include (but are not limited to) the following:

  • Novel algorithms and models for information extraction;
  • Integration of LLMs in text mining pipelines;
  • Named entity recognition and relation extraction;
  • Event detection and temporal information extraction;
  • Multilingual and cross-lingual information extraction;
  • Information extraction from scientific research, legal texts, clinical notes, or social media;
  • Text mining for knowledge base population and ontology learning;
  • Applications of information extraction in domains such as healthcare, finance, security, or education;
  • Benchmarking and evaluation of text mining/information extraction systems;
  • Ethics, bias, and fairness in text mining and information extraction.

Dr. Loukas Ilias
Dr. Dimitrios Askounis
Dr. Spiros Mouzakitis
Guest Editors

Manuscript Submission Information

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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-anonymized 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

  • text mining
  • information extraction
  • natural language processing (NLP)
  • named entity recognition (NER)
  • relation extraction
  • large language models (LLMs)
  • knowledge discovery
  • unstructured data analysis
  • domain-specific IE applications
  • multilingual text processing

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Published Papers (3 papers)

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Research

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27 pages, 2824 KB  
Article
De-Identification of Electronic Health Records Using Deep Learning and Transformers
by Fatih Dilmaç and Adil Alpkocak
Appl. Sci. 2026, 16(4), 1692; https://doi.org/10.3390/app16041692 - 8 Feb 2026
Viewed by 818
Abstract
Adoption of electronic health records (EHRs) has significantly advanced healthcare by enabling extensive data storage and analysis for clinical decisions and research. However, sensitive personally identifiable information (PII) within EHRs presents major challenges concerning patient privacy, data security, and regulatory compliance. Effective automated [...] Read more.
Adoption of electronic health records (EHRs) has significantly advanced healthcare by enabling extensive data storage and analysis for clinical decisions and research. However, sensitive personally identifiable information (PII) within EHRs presents major challenges concerning patient privacy, data security, and regulatory compliance. Effective automated de-identification techniques for detecting and removing protected health information (PHI) are thus essential. This study presents one of the first focused studies on Turkish EHR de-identification, comparing traditional sequence-based neural architectures with advanced transformer-based large language models (LLMs) for PHI detection. We introduce and publicly release a manually annotated benchmark dataset of TEHRs, covering diverse PHI types, supporting further research in Turkish clinical text. Two methodologies were evaluated: bidirectional long short-term memory (BiLSTM) models (with and without Conditional Random Fields (CRFs)) and six fine-tuned pre-trained LLMs. Experiments demonstrated the superior performance of transformer-based LLMs, achieving a macro F1 score of 92.20%, significantly outperforming traditional methods. Among sequence-based models, BiLSTM + CRF attained an 83.00% F1 score, exceeding the baseline BiLSTM 78.40%. Results highlight the potential of transformer-based models for privacy-preserving Turkish clinical text and underscore the importance of annotated benchmark datasets. Full article
(This article belongs to the Special Issue Text Mining with Information Extraction: Latest Advances and Prospects)
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25 pages, 2732 KB  
Article
Irony and Sarcasm Detection in Turkish Texts: A Comparative Study of Transformer-Based Models and Ensemble Learning
by Murat Eser and Metin Bilgin
Appl. Sci. 2025, 15(23), 12498; https://doi.org/10.3390/app152312498 - 25 Nov 2025
Cited by 2 | Viewed by 1753
Abstract
Irony and sarcasm are forms of expression that emphasize the inconsistency between what is said and what is meant. Correctly classifying such expressions is an important text mining problem, especially on user-centered platforms such as social media. Due to the increasing prevalence of [...] Read more.
Irony and sarcasm are forms of expression that emphasize the inconsistency between what is said and what is meant. Correctly classifying such expressions is an important text mining problem, especially on user-centered platforms such as social media. Due to the increasing prevalence of implicit expressions, this topic has become a significant area of research in Natural Language Processing (NLP). However, the simultaneous detection of ironic and sarcastic expressions is highly challenging, as both types of implicit sentiments often convey closely related meanings. To address the detection of irony and sarcasm, this study compares the performance of transformer-based models and an ensemble learning method on Turkish texts, using five textual datasets—monogram, bigram, trigram, quadrigram, and omnigram—that share the same textual content but differ in context length. To improve classification performance, an ensemble learning approach based on the Artificial Rabbit Optimization (ARO) algorithm was implemented, combining the outputs of the models to produce final predictions. The experimental results indicate that as the context width of the datasets increases, the models achieve better predictions, leading to improvements across all performance metrics. The ensemble learning method outperformed individual models in all metrics, with performance increasing as the context expanded, achieving the highest success in the omnigram dataset with 76.71% accuracy, 74.64% precision, 73.29% sensitivity, and 73.96% F-Score. This study demonstrates that both model architecture and data structure are decisive factors in text classification performance, showing that community methods can make significant contributions to the effectiveness of deep learning solutions in low-resource languages. Full article
(This article belongs to the Special Issue Text Mining with Information Extraction: Latest Advances and Prospects)
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Review

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36 pages, 2458 KB  
Review
Natural Language Processing in Breast Imaging Reports: A Scoping Review with Implications for Low-Resource Clinical Languages
by Seda Yıldırım, Erkan Ülker and Necdet Poyraz
Appl. Sci. 2026, 16(12), 5847; https://doi.org/10.3390/app16125847 - 10 Jun 2026
Viewed by 212
Abstract
Breast imaging reports are commonly recorded as unstructured free-text documents, which limits their secondary use for large-scale clinical analysis, structured information extraction, and clinical decision support. These challenges are particularly important in morphologically rich and low-resource clinical languages, where linguistic variability, inconsistent terminology, [...] Read more.
Breast imaging reports are commonly recorded as unstructured free-text documents, which limits their secondary use for large-scale clinical analysis, structured information extraction, and clinical decision support. These challenges are particularly important in morphologically rich and low-resource clinical languages, where linguistic variability, inconsistent terminology, and limited annotated corpora may reduce the direct applicability of existing natural language processing (NLP) approaches. This study presents a scoping review of NLP research on breast imaging reports, conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). Attention is given to BI-RADS (Breast Imaging Reporting and Data System)-related analytical tasks, methodological trends, dataset characteristics, evaluation practices, and implications for low-resource clinical NLP settings, including Turkish. A comprehensive literature search was conducted across Web of Science, Scopus, PubMed, IEEE Xplore, and Google Scholar in February 2026. To reduce the risk of missing recent studies using transformer- and large language model (LLM)-related terminology, a targeted supplementary search was also conducted. Following screening and eligibility assessment, 39 studies were included in the final synthesis. The findings show that the literature is concentrated mainly on BI-RADS classification/annotation and information extraction tasks. Task-wise, BI-RADS classification/annotation was the most frequent category, followed by information extraction. Methodologically, the reviewed literature shows a shift from rule-based and traditional machine learning approaches toward transformer- and LLM-based methods. LLM-based studies were frequently represented, particularly among recent studies and those identified through the targeted supplementary search; therefore, their observed prominence should be interpreted cautiously. Despite these advances, the literature remains linguistically imbalanced and methodologically heterogeneous. English was the most frequently represented report or dataset language, whereas Turkish breast imaging NLP studies remained limited. Major challenges identified across the reviewed studies include dataset heterogeneity, inconsistent annotation practices, variable evaluation metrics, limited external validation, and incomplete reporting of reproducibility-related details. This scoping review provides a structured synthesis of methodological trends, task categories, dataset characteristics, evaluation practices, and reproducibility-related limitations in breast imaging NLP. Overall, the findings highlight the need for better documented datasets, standardised evaluation practices, transparent reporting, clinically grounded validation, and stronger research efforts for low-resource clinical language settings. Full article
(This article belongs to the Special Issue Text Mining with Information Extraction: Latest Advances and Prospects)
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