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        <title>Clustering Analysis for Error Detection in Named Entity Recognition Datasets</title>
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        <namePart type="given">Matthew</namePart>
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        <namePart type="given">Timothy</namePart>
        <namePart type="family">Obiso</namePart>
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    <name type="personal">
        <namePart type="given">Sam</namePart>
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    <name type="personal">
        <namePart type="given">Constantine</namePart>
        <namePart type="family">Lignos</namePart>
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            <title>Proceedings of the 20th Linguistic Annotation Workshop (LAW XX)</title>
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            <namePart type="given">Yang</namePart>
            <namePart type="given">Janet</namePart>
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    <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-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.</abstract>
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%0 Conference Proceedings
%T Clustering Analysis for Error Detection in Named Entity Recognition Datasets
%A Flynn, Matthew
%A Obiso, Timothy
%A Newman, Sam
%A Lignos, Constantine
%Y Liu, Yang Janet
%Y Gessler, Luke
%S Proceedings of the 20th Linguistic Annotation Workshop (LAW XX)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-404-0
%F flynn-etal-2026-clustering
%X 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.
%U https://aclanthology.org/2026.law-main.17/
%P 229-240

Markdown (Informal)

[Clustering Analysis for Error Detection in Named Entity Recognition Datasets](https://aclanthology.org/2026.law-main.17/) (Flynn et al., LAW 2026)

• Clustering Analysis for Error Detection in Named Entity Recognition Datasets (Flynn et al., LAW 2026)

ACL

• Matthew Flynn, Timothy Obiso, Sam Newman, and Constantine Lignos. 2026. Clustering Analysis for Error Detection in Named Entity Recognition Datasets. In Proceedings of the 20th Linguistic Annotation Workshop (LAW XX), pages 229–240, San Diego, California, USA. Association for Computational Linguistics.