While machine learning methods for named entity recognition (mention-level detection) have become common, machine learning methods have rarely been applied to normalization (concept-level identification). Recent research introduced a machine learning method for normalization based on pairwise learning to rank. This method, DNorm, uses a linear 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 employ a dimensionality reduction technique based on low-rank matrix approximation, similar to latent semantic indexing. We compare the performance of the low rank method to previous work, using disease name normalization in the NCBI Disease Corpus as the test case, and demonstrate increased performance as the matrix rank increases. We further demonstrate a significant reduction in the number of parameters to be learned and discuss the implications of this result in the context of algorithm scalability.
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