CleanCoNLL: A Nearly Noise-Free Named Entity Recognition Dataset
Submission Type: Regular Long Paper
Submission Track: Resources and Evaluation
Keywords: Dataset Relabeling and Evaluation, Label Error Detection and Correction, Named Entity Recognition, CoNLL-03, Entity Linking
TL;DR: We present and evaluate CleanCoNLL, a revised version of the classic CoNLL-03 benchmark, that significantly improves annotation quality and label consistency, and adds a layer of entity linking information.
Abstract: The CoNLL-03 corpus is arguably the most well-known and utilized benchmark dataset for named entity recognition (NER). However, prior works found significant numbers of annotation errors, incompleteness, and inconsistencies in the data. This poses challenges to objectively comparing NER approaches and analyzing their errors, as current state-of-the-art models achieve F1-scores that are comparable to or even exceed the estimated noise level in CoNLL-03. To address this issue, we present a comprehensive relabeling effort assisted by automatic consistency checking that corrects 7.0% of all labels in the English CoNLL-03. Our effort adds a layer of entity linking annotation both for better explainability of NER labels and as additional safeguard of annotation quality. Our experimental evaluation finds not only that state-of-the-art approaches reach significantly higher F1-scores (97.1%) on our data, but crucially that the share of correct predictions falsely counted as errors due to annotation noise drops from 47% to 6%. This indicates that our resource is well suited to analyze the remaining errors made by state-of-the-art models, and that the theoretical upper bound even on high resource, coarse-grained NER is not yet reached.
To facilitate such analysis, we make CleanCoNLL publicly available to the research community.
Submission Number: 1583
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