  We conducted a scoping review for active learning in the domain of natural
language processing (NLP), which we summarize in accordance with the PRISMA-ScR
guidelines as follows:
  Objective: Identify active learning strategies that were proposed for entity
recognition and their evaluation environments (datasets, metrics, hardware,
execution time). Design: We used Scopus and ACM as our search engines. We
compared the results with two literature surveys to assess the search quality.
We included peer-reviewed English publications introducing or comparing active
learning strategies for entity recognition. Results: We analyzed 62 relevant
papers and identified 106 active learning strategies. We grouped them into
three categories: exploitation-based (60x), exploration-based (14x), and hybrid
strategies (32x). We found that all studies used the F1-score as an evaluation
metric. Information about hardware (6x) and execution time (13x) was only
occasionally included. The 62 papers used 57 different datasets to evaluate
their respective strategies. Most datasets contained newspaper articles or
biomedical/medical data. Our analysis revealed that 26 out of 57 datasets are
publicly accessible.
  Conclusion: Numerous active learning strategies have been identified, along
with significant open questions that still need to be addressed. Researchers
and practitioners face difficulties when making data-driven decisions about
which active learning strategy to adopt. Conducting comprehensive empirical
comparisons using the evaluation environment proposed in this study could help
establish best practices in the domain.
