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Boosting Biomedical Concept Extraction by Rule-Based Data Augmentation

arXiv 2407.02719 ↗First posted 3 Jul 2024cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 46 references that are in the map. First posted on arXiv 3 Jul 2024.

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References · 24

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12 more
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