  This work addresses challenges arising from extracting entities from textual
data, including the high cost of data annotation, model accuracy, selecting
appropriate evaluation criteria, and the overall quality of annotation. We
present a framework that integrates Entity Set Expansion (ESE) and Active
Learning (AL) to reduce the annotation cost of sparse data and provide an
online evaluation method as feedback. This incremental and interactive learning
framework allows for rapid annotation and subsequent extraction of sparse data
while maintaining high accuracy. We evaluate our framework on three publicly
available datasets and show that it drastically reduces the cost of sparse
entity annotation by an average of 85% and 45% to reach 0.9 and 1.0 F-Scores
respectively. Moreover, the method exhibited robust performance across all
datasets.
