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
Session
Year 2014
Authors

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...