Abstract
This article demonstrates the benefits of using sequence representations based on word embeddings to inform the seed selection and sample selection processes in an active learning pipeline for clinical information extraction. Seed selection refers to choosing an initial sample set to label to form an initial learning model. Sample selection refers to selecting informative samples to update the model at each iteration of the active learning process. Compared to supervised machine learning approaches, active learning offers the opportunity to build statistical classifiers with a reduced amount of training samples that require manual annotation. Reducing the manual annotation effort can support automating the clinical information extraction process. This is particularly beneficial in the clinical domain, where manual annotation is a time‐consuming and costly task, as it requires extensive labor from clinical experts. Our empirical findings demonstrate that (a) using sequence representations along with the length of sequence for seed selection shows potential towards more effective initial models, and (b) using sequence representations for sample selection leads to significantly lower manual annotation efforts, with up to 3% and 6% fewer tokens and concepts requiring annotation, respectively, compared to state‐of‐the‐art query strategies.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Journal of the Association for Information Science and Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.