@inproceedings{flynn-etal-2026-clustering,
    title = "Clustering Analysis for Error Detection in Named Entity Recognition Datasets",
    author = "Flynn, Matthew  and
      Obiso, Timothy  and
      Newman, Sam  and
      Lignos, Constantine",
    editor = "Liu, Yang Janet  and
      Gessler, Luke",
    booktitle = "Proceedings of the 20th Linguistic Annotation Workshop ({LAW} {XX})",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.law-main.17/",
    pages = "229--240",
    ISBN = "979-8-89176-404-0",
    abstract = "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-\textit{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 \url{https://github.com/bltlab/clustering-for-ner}."
}