Boosting Biomedical Concept Extraction by Rule-Based Data Augmentation
<|reference_start|>Boosting Biomedical Concept Extraction by Rule-Based Data Augmentation: Document-level biomedical concept extraction is the task of identifying biomedical concepts mentioned in a given document. Recent advancements have adapted pre-trained language models for this task. However, the scarcity of domain-specific data and the deviation of concepts from their canonical names often hinder these models' effectiveness. To tackle this issue, we employ MetaMapLite, an existing rule-based concept mapping system, to generate additional pseudo-annotated data from PubMed and PMC. The annotated data are used to augment the limited training data. Through extensive experiments, this study demonstrates the utility of a manually crafted concept mapping tool for training a better concept extraction model.<|reference_end|>
@article{shao2024boosting,
    title={Boosting Biomedical Concept Extraction by Rule-Based Data Augmentation},
    author={Qiwei Shao, Fengran Mo and Jian-Yun Nie},
    journal={arXiv preprint arXiv:2407.02719},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02719},
    primaryClass={cs.CL}
}