Papers › Boundary Smoothing for Named Entity Recognition
Boundary Smoothing for Named Entity Recognition
Enwei Zhu, Jinpeng Li
Neural named entity recognition (NER) models may easily encounter the over-confidence issue, which degrades the performance and calibration. Inspired by label smoothing and driven by the ambiguity of boundary annotation in NER engineering, we propose boundary smoothing as a regularization technique for span-based neural NER models. It re-assigns entity probabilities from annotated spans to the surrounding ones. Built on a simple but strong baseline, our model achieves results better than or competitive with previous state-of-the-art systems on eight well-known NER benchmarks. Further empirical analysis suggests that boundary smoothing effectively mitigates over-confidence, improves model calibration, and brings flatter neural minima and more smoothed loss landscapes.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Chinese Named Entity Recognition | MSRA | Baseline + BS | F1 | 96.26 | #3 of 21 | Archive leaderboard | report |
| Chinese Named Entity Recognition | OntoNotes 4 | Baseline + BS | F1 | 82.83 | #3 of 15 | Archive leaderboard | report |
| Chinese Named Entity Recognition | Resume NER | Baseline + BS | F1 | 96.66 | #3 of 13 | Archive leaderboard | report |
| Chinese Named Entity Recognition | Weibo NER | Baseline + BS | F1 | 72.66 | #2 of 18 | Archive leaderboard | report |
| Named Entity Recognition (NER) | CoNLL 2003 (English) | Baseline + BS | F1 | 93.65 | #15 of 73 | Archive leaderboard | report |
| Named Entity Recognition (NER) | Ontonotes v5 (English) | Baseline + BS | F1 | 91.74 | #3 of 28 | Archive leaderboard | report |
| Nested Named Entity Recognition | ACE 2004 | Baseline + BS | F1 | 87.98 | #8 of 24 | Archive leaderboard | report |
| Nested Named Entity Recognition | ACE 2005 | Baseline + BS | F1 | 87.15 | #5 of 25 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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