@inproceedings{martinelli-etal-2026-domain,
    title = "A Domain-Specific Curated Benchmark for Entity and Document-Level Relation Extraction",
    author = "Martinelli, Marco  and
      Marchesin, Stefano  and
      Bonato, Vanessa  and
      Di Nunzio, Giorgio  and
      Ferro, Nicola  and
      Irrera, Ornella  and
      Menotti, Laura  and
      Vezzani, Federica  and
      Silvello, Gianmaria",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.301/",
    pages = "5693--5711",
    ISBN = "979-8-89176-386-9",
    abstract = "Information Extraction (IE), encompassing Named Entity Recognition (NER), Named Entity Linking (NEL), and Relation Extraction (RE), is critical for transforming the rapidly growing volume of scientific publications into structured, actionable knowledge. This need is especially evident in fast-evolving biomedical fields such as the gut-brain axis, where research investigates complex interactions between the gut microbiota and brain-related disorders. Existing biomedical IE benchmarks, however, are often narrow in scope and rely heavily on distantly supervised or automatically generated annotations, limiting their utility for advancing robust IE methods. We introduce GutBrainIE, a benchmark based on more than 1,600 PubMed abstracts, manually annotated by biomedical and terminological experts with fine-grained entities, concept-level links, and relations. While grounded in the gut-brain axis, the benchmark{'}s rich schema, multiple tasks, and combination of highly curated and weakly supervised data make it broadly applicable to the development and evaluation of biomedical IE systems across domains."
}