@inproceedings{phan-nguyen-2022-simple,
    title = "Simple Semantic-based Data Augmentation for Named Entity Recognition in Biomedical Texts",
    author = "Phan, Uyen  and
      Nguyen, Nhung",
    editor = "Demner-Fushman, Dina  and
      Cohen, Kevin Bretonnel  and
      Ananiadou, Sophia  and
      Tsujii, Junichi",
    booktitle = "Proceedings of the 21st Workshop on Biomedical Language Processing",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.bionlp-1.12/",
    doi = "10.18653/v1/2022.bionlp-1.12",
    pages = "123--129",
    abstract = "Data augmentation is important in addressing data sparsity and low resources in NLP. Unlike data augmentation for other tasks such as sentence-level and sentence-pair ones, data augmentation for named entity recognition (NER) requires preserving the semantic of entities. To that end, in this paper we propose a simple semantic-based data augmentation method for biomedical NER. Our method leverages semantic information from pre-trained language models for both entity-level and sentence-level. Experimental results on two datasets: i2b2-2010 (English) and VietBioNER (Vietnamese) showed that the proposed method could improve NER performance."
}