# Weakly-Supervised Multilingual Medical NER for Symptom Extraction for Low-Resource Languages
## 1 Introduction
The model in bert-medical-ner ([[https://huggingface.co/silpakanneganti/bert-medical-ner](https://huggingface.co/silpakanneganti/bert-medical-ner)]([https://huggingface.co/silpakanneganti/bert-medical-ner](https://huggingface.co/silpakanneganti/bert-medical-ner)), accessed on 14 April 2025) on the HuggingFace ([[https://huggingface.co/](https://huggingface.co/)]([https://huggingface.co/](https://huggingface.co/)), accessed on 14 April 2025) platform extracts the entities, such as age, sex, clinical event, non-biological location, duration, severity, biological structure, sign symptom, and more, achieving a self-reported F1 score of 67.5%. Similarly, bert-base-uncased_clinical-ner ([[https://huggingface.co/samrawal/bert-base-uncased_clinical-ner](https://huggingface.co/samrawal/bert-base-uncased_clinical-ner)]([https://huggingface.co/samrawal/bert-base-uncased_clinical-ner](https://huggingface.co/samrawal/bert-base-uncased_clinical-ner)), accessed on 14 April 2025) is another NER model, which extracts problem, treatment, and test entities from medical texts but it does not report any performance evaluation, leaving its effectiveness unclear. Other models, such as the one proposed in [16] and [PERSON]'s clinical i2b2 model [17; 18], report F1 scores of 81.2% and 88.1%, respectively, yet their focus remains restricted to English medical texts. Domain-specific models like biobert_ncbi_disease_ner ([[https://huggingface.co/ugaray96/biobert_ncbi_disease_ner](https://huggingface.co/ugaray96/biobert_ncbi_disease_ner)]([https://huggingface.co/ugaray96/biobert_ncbi_disease_ner](https://huggingface.co/ugaray96/biobert_ncbi_disease_ner)), accessed on 14 April 2025) and en_ner_bc5 cdr_md ([[https://huggingface.co/Kaelan/en_ner_bc5](https://huggingface.co/Kaelan/en_ner_bc5) cdr_md]([https://huggingface.co/Kaelan/en_ner_bc5](https://huggingface.co/Kaelan/en_ner_bc5) cdr_md), accessed on 14 April 2025) are tailored for tasks like disease or chemical recognition, reporting F1 scores of 85.7% for chemicals and diseases. Moreover, they are well-tuned to medical semantics but not to the way individuals express or describe their symptoms. Efforts in non-English languages include models like GERNEMED [19] for German, which extractsentities such as drug dosage and frequency (F1: 81.5%), MedPsyNIT [20] for Italian, which extracts symptoms and comorbidities (F1: 89.5%), and the model in [21] for Spanish, which recognizes anatomical, chemical, and pathological entities (F1: 86.4%). The model for Portuguese achieves a high F1 score of 92.6% [22] for extracting entities like disorders, medical procedures, and pharmacologic substances. Despite these successes, multilingual models for medical NER remain scarce, and no models currently exist for Greek, Polish, or Slovenian. In summary, while existing clinical NER models demonstrate strong performance in specific settings, they are constrained by their focus on a single language or a narrow range of languages, their reliance on extensive annotated datasets that are expensive and time-intensive to create, and their limited ability to adapt across different types of texts, such as formal medical documents and informal everyday language. Our aim is to make the following contributions:  
* we introduce a flexible, multilingual, and adaptable pipeline for medical NER;
* our pipeline enables effective extraction of medical entities (problem, test, and treatment) from a given body of text;
* our pipeline is effective across diverse languages, with a focus on low-resource languages. Our pipeline is designed to handle both formal and informal textual contexts.