@inproceedings{sasikumar-mantri-2023-transfer,
title = "Transfer Learning for Low-Resource Clinical Named Entity Recognition",
author = "Sasikumar, Nevasini and
Mantri, Krishna Sri Ipsit",
editor = "Naumann, Tristan and
Ben Abacha, Asma and
Bethard, Steven and
Roberts, Kirk and
Rumshisky, Anna",
booktitle = "Proceedings of the 5th Clinical Natural Language Processing Workshop",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.clinicalnlp-1.53",
doi = "10.18653/v1/2023.clinicalnlp-1.53",
pages = "514--518",
abstract = "We propose a transfer learning method that adapts a high-resource English clinical NER model to low-resource languages and domains using only small amounts of in-domain annotated data. Our approach involves translating in-domain datasets to English, fine-tuning the English model on the translated data, and then transferring it to the target language/domain. Experiments on Spanish, French, and conversational clinical text datasets show accuracy gains over models trained on target data alone. Our method achieves state-of-the-art performance and can enable clinical NLP in more languages and modalities with limited resources.",
}
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<abstract>We propose a transfer learning method that adapts a high-resource English clinical NER model to low-resource languages and domains using only small amounts of in-domain annotated data. Our approach involves translating in-domain datasets to English, fine-tuning the English model on the translated data, and then transferring it to the target language/domain. Experiments on Spanish, French, and conversational clinical text datasets show accuracy gains over models trained on target data alone. Our method achieves state-of-the-art performance and can enable clinical NLP in more languages and modalities with limited resources.</abstract>
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%0 Conference Proceedings
%T Transfer Learning for Low-Resource Clinical Named Entity Recognition
%A Sasikumar, Nevasini
%A Mantri, Krishna Sri Ipsit
%Y Naumann, Tristan
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Roberts, Kirk
%Y Rumshisky, Anna
%S Proceedings of the 5th Clinical Natural Language Processing Workshop
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F sasikumar-mantri-2023-transfer
%X We propose a transfer learning method that adapts a high-resource English clinical NER model to low-resource languages and domains using only small amounts of in-domain annotated data. Our approach involves translating in-domain datasets to English, fine-tuning the English model on the translated data, and then transferring it to the target language/domain. Experiments on Spanish, French, and conversational clinical text datasets show accuracy gains over models trained on target data alone. Our method achieves state-of-the-art performance and can enable clinical NLP in more languages and modalities with limited resources.
%R 10.18653/v1/2023.clinicalnlp-1.53
%U https://aclanthology.org/2023.clinicalnlp-1.53
%U https://doi.org/10.18653/v1/2023.clinicalnlp-1.53
%P 514-518
Markdown (Informal)
[Transfer Learning for Low-Resource Clinical Named Entity Recognition](https://aclanthology.org/2023.clinicalnlp-1.53) (Sasikumar & Mantri, ClinicalNLP 2023)
ACL