Boosting Biomedical Concept Extraction by Rule-Based Data Augmentation

TL;DR

This paper enhances biomedical concept extraction by using rule-based data augmentation with MetaMapLite to generate pseudo-annotations, improving model training despite limited domain-specific data.

Contribution

It introduces a novel approach of leveraging a rule-based system for data augmentation to improve biomedical concept extraction models.

Findings

Augmented data improves extraction accuracy.

Rule-based augmentation outperforms baseline models.

Demonstrated effectiveness on PubMed and PMC datasets.

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.