An active learning-enabled annotation system for clinical named entity recognition
Abstract Background Active learning (AL) has shown the promising potential to minimize the annotation cost while maximizing the performance in building statistical natural language processing (NLP) models. However, very few studies have investigated AL in a real-life setting in medical domain. Metho...
Main Authors: | , , , , , , , , , , |
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Format: | Article |
Language: | English |
Published: |
BMC
2017-07-01
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Series: | BMC Medical Informatics and Decision Making |
Online Access: | http://link.springer.com/article/10.1186/s12911-017-0466-9 |