Before revision: Motivation: Recognizing named entities (NER) and their associated attributes like negation are core tasks in natural language processing.However, manually labeling data for entity tasks is time consuming and expensive, creating barriers to using machine learning in new medical applications.
After revision: In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e.g. the order of an event relative to a time index) can inform many important analyses. However, creating training data for clinical entity tasks is time consuming and expensive, creating barriers to using machine learning in new medical applications.