@inproceedings{jonker-matos-2026-bit,
    title = "{BIT}.{UA} at {\#}{SMM}4{H}{--}{H}ea{RD} 2026: Towards Multi-Class Multilingual Clinical Entity Recognition with Multi-Head {CRF} Ensembles",
    author = "Jonker, Richard A. A.  and
      Matos, S{\'e}rgio",
    editor = "Lopez-Garcia, Guillermo  and
      Gonzalez-Hernandez, Graciela",
    booktitle = "Proceedings of the 11th Social Media Mining for Health Research and Applications ({SMM}4{H}-{H}ea{RD} 2026) Workshop and Shared Tasks",
    month = jul,
    year = "2026",
    address = "San Diego, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.smm4h-1.8/",
    pages = "41--48",
    ISBN = "979-8-89176-432-3",
    abstract = "This paper describes the BIT.UA system for the MultiClinNER shared task at {\#}SMM4H{--}HeaRD 2026, targeting multilingual clinical named entity recognition across seven languages for three entity types (Disease, Procedure, Symptom). We extend the Multi-Head CRF architecture, originally developed for multi-class NER on Spanish clinical text, to the multilingual setting. To enable joint multi-entity training despite per-entity text variations in the dataset, we develop an adaptive text consolidation pipeline that preserves over 94{\%} of annotations. Our central finding is that a single xlm-roberta-large model, trained jointly on all seven languages and three entity types, achieves competition rank 2 for five of seven languages, outperforming dedicated monolingual models by up to +6.94 F1 points, while requiring only a single set of weights. Ensembling multiple seeds of this model achieves rank 1 for those five languages, and combining it with monolingual models yields rank 1 for the remaining two. Code and models are publicly available at https://github.com/ieeta-pt/Multi-Head-CRF/tree/MultiClinNER and https://huggingface.co/collections/IEETA/multiclinner-models."
}