Impact of Stop Sets on Stopping Active Learning for Text Classification. (arXiv:2201.05460v1 [cs.IR])

Active learning is an increasingly important branch of machine learning and a
powerful technique for natural language processing. The main advantage of
active learning is its potential to reduce the amount of labeled data needed to
learn high-performing models. A vital aspect of an effective active learning
algorithm is the determination of when to stop obtaining additional labeled
data. Several leading state-of-the-art stopping methods use a stop set to help
make this decision. However, there has been relatively less attention …

active learning arxiv classification impact learning text text classification