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arXiv · 2407.02719

Boosting Biomedical Concept Extraction by Rule-Based Data Augmentation

Qiwei Shao·Fengran Mo·Jian-Yun Nie
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Abstract

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.

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Qiwei Shao, Fengran Mo, Jian-Yun Nie. 2024-07-03. Boosting Biomedical Concept Extraction by Rule-Based Data Augmentation. https://arxiv.org/abs/2407.02719

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