  Discriminating the matched named entity pairs or identifying the entities'
canonical forms are critical in text mining tasks. More precise named entity
normalization in text mining will benefit other subsequent text analytic
applications. We built the named entity normalization model with a novel Edge
Weight Updating Neural Network. Our proposed model when tested on four
different datasets achieved state-of-the-art results. We, next, verify our
model's performance on NCBI Disease, BC5CDR Disease, and BC5CDR Chemical
databases, which are widely used named entity normalization datasets in the
bioinformatics field. We also tested our model with our own financial named
entity normalization dataset to validate the efficacy for more general
applications. Using the constructed dataset, we differentiate named entity
pairs. Our model achieved the highest named entity normalization performances
in terms of various evaluation metrics.
