Interactive medical word sense disambiguation through informed learning.

ABSTRACT:

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.

Methods

We developed an interactive learning algorithm with expert labeling instances and features. An expert can provide supervision in 3 ways: labeling instances, specifying indicative words of a sense, and highlighting supporting evidence in a labeled instance. The algorithm learns from these labels and iteratively selects the most informative instances to ask for future labels. Our evaluation used 3 WSD corpora: 198 ambiguous terms from Medical Subje

SUBMITTER: Wang Y 

PROVIDER: S-EPMC6658868 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature