@inproceedings{meng-etal-2023-crf,
    title = "{CRF}-based recognition of invasive fungal infection concepts in {CHIFIR} clinical reports",
    author = "Meng, Yang  and
      Rozova, Vlada  and
      Verspoor, Karin",
    editor = "Muresan, Smaranda  and
      Chen, Vivian  and
      Casey, Kennington  and
      David, Vandyke  and
      Nina, Dethlefs  and
      Koji, Inoue  and
      Erik, Ekstedt  and
      Stefan, Ultes",
    booktitle = "Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association",
    month = nov,
    year = "2023",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2023.alta-1.15/",
    pages = "130--135",
    abstract = "Named entity recognition (NER) in clinical documentation is often hindered by the use of highly specialised terminology, variation in language used to express medical findings and general scarcity of high-quality data available for training. This short paper compares a Conditional Random Fields model to the previously established dictionary-based approach and evaluates its ability to extract information from a small corpus of annotated pathology reports. The results suggest that including token descriptors as well as contextual features significantly improves precision on several concept categories while maintaining the same level of recall."
}