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        <title>End-to-End Deep Learning for Named Entity Recognition and Relation Extraction in Gut-Brain Axis PubMed Abstracts</title>
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    <abstract>This is a comparative study tackling named entity recognition and relation extraction from PubMed abstracts with focus on the gut-brain interplay. The proposed systems for named entity recognition cover a range of models and techniques from traditional gazetteer-based approaches, transformer-based approaches, transformer domain adaptation, large models pre-training as well as LLM prompting. The best performing model among these achieves 82.53% F1-score. The relation extraction task is addressed with ATLOP and LLMs and their best results reach F1 up to 63.80% on binary relation extraction, 89.40% on ternary tag-based relation extraction and 40.32% on ternary mention-based relation extraction.</abstract>
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%0 Conference Proceedings
%T End-to-End Deep Learning for Named Entity Recognition and Relation Extraction in Gut-Brain Axis PubMed Abstracts
%A Datseris, Aleksis Ioannis
%A Kuzmanov, Mario
%A Nikolova-Koleva, Ivelina
%A Taskov, Dimitar
%A Boytcheva, Svetla
%Y Angelova, Galia
%Y Kunilovskaya, Maria
%Y Escribe, Marie
%Y Mitkov, Ruslan
%S Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F datseris-etal-2025-end
%X This is a comparative study tackling named entity recognition and relation extraction from PubMed abstracts with focus on the gut-brain interplay. The proposed systems for named entity recognition cover a range of models and techniques from traditional gazetteer-based approaches, transformer-based approaches, transformer domain adaptation, large models pre-training as well as LLM prompting. The best performing model among these achieves 82.53% F1-score. The relation extraction task is addressed with ATLOP and LLMs and their best results reach F1 up to 63.80% on binary relation extraction, 89.40% on ternary tag-based relation extraction and 40.32% on ternary mention-based relation extraction.
%U https://aclanthology.org/2025.ranlp-1.31/
%P 254-259

Markdown (Informal)

[End-to-End Deep Learning for Named Entity Recognition and Relation Extraction in Gut-Brain Axis PubMed Abstracts](https://aclanthology.org/2025.ranlp-1.31/) (Datseris et al., RANLP 2025)

• End-to-End Deep Learning for Named Entity Recognition and Relation Extraction in Gut-Brain Axis PubMed Abstracts (Datseris et al., RANLP 2025)

ACL

• Aleksis Ioannis Datseris, Mario Kuzmanov, Ivelina Nikolova-Koleva, Dimitar Taskov, and Svetla Boytcheva. 2025. End-to-End Deep Learning for Named Entity Recognition and Relation Extraction in Gut-Brain Axis PubMed Abstracts. In Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era, pages 254–259, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.