BioASQ

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The aim of the BioASQ Lab is to push the research frontier towards systems that use the diverse and voluminous information available online to respond directly to the information needs of biomedical scientists.

Tasks

The BioASQ Lab features six different tasks:

Biomedical Semantic Question Answering
Benchmark datasets of biomedical questions, in English, along with gold standard (reference) answers constructed by a team of biomedical experts. The participants have to respond with relevant articles, and snippets from designated resources, as well as exact and “ideal” answers.
Synergy: Question Answering for developing problems
Biomedical experts pose unanswered questions for developing problems, such as COVID-19, receive the responses provided by the participating systems, and provide feedback, together with updated questions in an iterative procedure that aims to facilitate the incremental understanding of developing problems in biomedicine and public health.
MultiClinSum-2: Multilingual Clinical Summarization
A shared task on the automatic summarization of lengthy clinical case reports written in different languages. The organizers distribute lengthy clinical case reports written in English, Spanish, French, Portuguese, German, Dutch, Catalan, Swedish, Norwegian and Italian. The participants generate summaries of the clinical case reports. The evaluation is based on a comparison with manual summaries of the clinical case reports.
BioNNE-R: Nested Relation Extraction in Russian and English
A shared task on NLP challenges in nested entity linking and relation extraction for English and Russian languages. The training and development sets will include relations among the most popular entities found in the NEREL-BIO dataset, such as disorders, anatomical structures, procedures, and chemicals. The evaluation is based on a comparison with manual nested relation annotations.
ElCardioCC: Clinical Coding in Cardiology
The ELCardioCC 2026 shared task concerns the automatic assignment of cardiology-related ICD-10 codes to hospital discharge letters at the document level. The dataset comprises 5,000 documents for training and development and 1,000 documents for testing.
GutBrainIE: Gut-Brain Interplay Information Extraction
The GutBrainIE task aims to foster the development of Information Extraction (IE) systems that support experts by automatically extracting and linking knowledge from scientific literature, facilitating the understanding of gut-brain interplay and its role in neurological disease. The task is divided into three subtasks: i) extraction of named entities, ii) identifying binary relations between entity pairs, and iii) linking entities to concepts in a reference ontology.

Organizers

  • Anastasia Krithara (National Center for Scientific Research “Demokritos”, Greece)
  • Anastasios Nentidis (National Center for Scientific Research “Demokritos”, Greece)
  • Martin Krallinger (Barcelona Supercomputing Center, Spain)
  • Miguel Rodriguez Ortega (Barcelona Supercomputing Center, Spain)
  • Elena Tutubalina (Artificial Intelligence Research Institute, Russia & Kazan Federal University, Russia )
  • Natalia Loukachevitch (Moscow State University, Russia)
  • Igor Rozhkov (Moscow State University, Russia)
  • Giorgio Maria Di Nunzio (University of Padua, Italy)
  • Nicola Ferro (University of Padua, Italy)
  • Stefano Marchesin (University of Padua, Italy)
  • Marco Martinelli (University of Padua, Italy)
  • Gianmaria Silvello (University of Padua, Italy)
  • Grigorios Tsoumakas (Aristotle University of Thessaloniki, Greece)
  • George Giannakoulas (Aristotle University of Thessaloniki, Greece)
  • Dimitris Dimitriadis (Aristotle University of Thessaloniki, Greece)
  • Alexandra Bekiaridou (Northwell Health, USA)
  • Athanasios Samaras (Aristotle University of Thessaloniki, Greece)
  • Vasiliki Patsiou (Aristotle University of Thessaloniki, Greece)
  • Georgios Paliouras (National Center for Scientific Research “Demokritos”, Greece)