Text Mining with Information Extraction: Latest Advances and Prospects

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

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