Papers › An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition
An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition
Hang Yan, Yu Sun, Xiaonan Li, Xipeng Qiu
Named entity recognition (NER) is the task to detect and classify the entity spans in the text. When entity spans overlap between each other, this problem is named as nested NER. Span-based methods have been widely used to tackle the nested NER. Most of these methods will get a score n ×n matrix, where n means the length of sentence, and each entry corresponds to a span. However, previous work ignores spatial relations in the score matrix. In this paper, we propose using Convolutional Neural Network (CNN) to model these spatial relations in the score matrix. Despite being simple, experiments in three commonly used nested NER datasets show that our model surpasses several recently proposed methods with the same pre-trained encoders. Further analysis shows that using CNN can help the model find more nested entities. Besides, we found that different papers used different sentence tokenizations for the three nested NER datasets, which will influence the comparison. Thus, we release a pre-processing script to facilitate future comparison.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Nested Named Entity Recognition | ACE 2004 | CNN-NER | F1 | 88.03 | #7 of 24 | Archive leaderboard | report |
| Nested Named Entity Recognition | ACE 2005 | CNN-NER | F1 | 87.42 | #4 of 25 | Archive leaderboard | report |
| Nested Named Entity Recognition | GENIA | CNN-NER | F1 | 81.40 | #3 of 26 | 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.
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