Interactive medical word sense disambiguation through informed learning

Objective Medical word sense disambiguation (WSD) is challenging and often requires significant training with data labeled by domain experts. This work aims to develop an interactive learning algorithm that makes efficient use of expert’s domain knowledge in building high-quality medical WSD models with minimal human effort.