A study of active learning methods for named entity recognition in clinical text
We developed novel active learning algorithms for clinical named entity recognition. Equal cost per sample is not a practical annotation cost assumption in this task. We evaluated methods based on two types of estimated annotation cost. To achieve 0.8 in F-measure, active learning could save 42% annotation cost in words. The actual benefit of active learning should be further evaluated in real time.
active learning clinical named entity recognition machine learning clinical natural language processing