Paper: Automated Disease Normalization with Low Rank Approximations

ACL ID W14-3404
Title Automated Disease Normalization with Low Rank Approximations
Venue Proceedings of the BioNLP Shared Task 2013 Workshop
Year 2014

While machine learning methods for named entity recognition (mention-level detection) have become common, ma- chine learning methods have rarely been applied to normalization (concept-level identification). Recent research intro- duced a machine learning method for normalization based on pairwise learning to rank. This method, DNorm, uses a lin- ear model to score the similarity between mentions and concept names, and has several desirable properties, including learning term variation directly from training data. In this manuscript we em- ploy a dimensionality reduction tech- nique based on low-rank matrix approx- imation, similar to latent semantic index- ing. We compare the performance of the low rank method to previous work, using disease name normalization in the NCBI Dise...