Clinical Word Sense Disambiguation with Interactive Search and Classification

Abstract

Resolving word ambiguity in clinical text is critical for many natural language processing applications. Effective word sense disambiguation (WSD) systems rely on training a machine learning based classifier with abundant clinical text that is accurately annotated, the creation of which can be costly and time-consuming. We describe a double-loop interactive machine learning process, named ReQ-ReC (ReQuery-ReClassify), and demonstrate its effectiveness on multiple evaluation corpora. Using ReQ-ReC, a human expert first uses her domain knowledge to include sense-specific contextual words into the ReQuery loops and searches for instances relevant to the senses. Then, in the ReClassify loops, the expert only annotates the most ambiguous instances found by the current WSD model. Even with machine-generated queries only, the framework is comparable with or faster than current active learning methods in building WSD models. The process can be further accelerated when human experts use their domain knowledge to guide the search process.