@inproceedings{ningtyas-etal-2026-annohid,
    title = "{A}nno{HID}: {LLM}-Assisted Annotation Framework for Low-Resource Medical Texts",
    author = "Ningtyas, Annisa Maulida  and
      Herwanto, Guntur Budi  and
      Sari, Yunita  and
      Putri, Rifki Afina  and
      Kovacevic, Filip  and
      El-Ebshihy, Alaa  and
      Arzt, Varvara  and
      Piroi, Florina",
    editor = "Durrett, Greg  and
      Jian, Ping",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-demo.67/",
    pages = "683--691",
    ISBN = "979-8-89176-392-0",
    abstract = "This paper introduces AnnoHID, a semi-automated annotation framework designed for medical texts in low-resource languages. The system integrates large language models (LLMs) for pre-annotation and human validation to support efficient and consistent annotation. We demonstrate its application to Bahasa Indonesia medical social media texts from Alodokter, a medical Q A platform, for Named Entity Recognition (NER) and Medical Concept Normalization (MCN). We conducted a user study with 21 participants to demonstrate the effectiveness of AnnoHID. The results show that LLM-assisted annotation yields higher inter-annotator agreement for both NER ($\kappa$ = 0.76) and MCN ($\kappa$ = 0.63) and that human review improves raw LLM NER output, raising the F1 score from 0.39 to 0.45. However, LLM assistance did not reduce annotation time and may introduce normalization bias in MCN. The framework is multilingual, human-in-the-loop, and interoperable with standard medical terminologies, such as SNOMED-CT. Future work focuses on mitigating pre-annotation bias, reducing annotation overhead, and scaling evaluations to larger datasets and additional low-resource languages."
}