This paper introduces a method for the automatic detection of annotation errors and corrections in named entity recognition datasets using a novel two-stage dimension reduction of dense sentence embeddings. We first find the top-n principal components of an embedding and then use UMAP for second-stage, non-linear dimension reduction and clustering using different distance metrics. We analyze these clusters using silhouette scores to flag outlier mentions for correction. Using the corrections in the CoNLL# dataset as a benchmark, all of the top-five outliers needed correction, as did 7 of the top-10. This approach also identified 32 of the top-50 outlier mentions that are corrections. This method offers a relatively low-effort way to leverage text embeddings and dimensionality reduction to identify likely annotation errors. We release related code and data at https://github.com/bltlab/clustering-for-ner.