GutBrainIE 2025 – Gut Brain Interplay Information Extraction @ CLEF 2025

GutBrainIE 2025 – Gut Brain Interplay Information Extraction @ CLEF 2025 Dataset Description Recent studies link the gut microbiota to mental health conditions and to neurodegenerative diseases such as Parkinson's and Alzheimer's. However, the rapid speed at which this research field is evolving presents a significant challenge for clinicians and researchers who have to keep pace with an ever-expanding volume of biomedical literature. In this context, automatic tools for extracting and structuring information from scientific texts are becoming essential to support the understanding of the gut-brain axis. GutBrainIE promotes the development of Natural Language Processing (NLP) systems capable of extracting structured specialized knowledge from biomedical texts related to the gut-brain axis, aiming to accelerate biomedical discoveries through automated Information Extraction (IE). Supported Tasks GutBrainIE proposes four tasks of increasing complexity: Named Entity Recognition (NER): systems have to identify and classify specific text spans (entity mentions) into one of the 13 predefined categories (e.g., bacteria, chemical, microbiota). Binary Tag-based Relation Extraction (BT-RE): systems are provided with a set of predefined relation types, each defined by a combination of compatible head and tail entities (e.g., Chemical -> Microbiome via Impact or Produced by), and have asked to identify which entities are in relation within a document, without specifying the exact predicate or entity mentions involved. Ternary Tag-based Relation Extraction (TT-RE): this task extends BT-RE by requiring participants to predict the specific relation predicate connecting each head-tail entity pair. Ternary Mention-based Relation Extraction (TM-RE): this is the most challenging task, demanding to identify the exact entity mention involved in a relation and assign the correct relation predicate.