  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.
