Papers › Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention...
Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism
Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao, Shengping Liu
Named entity recognition (NER) is an important task in natural language processing area, which needs to determine entities boundaries and classify them into pre-defined categories. For Chinese NER task, there is only a very small amount of annotated data available. Chinese NER task and Chinese word segmentation (CWS) task have many similar word boundaries. There are also specificities in each task. However, existing methods for Chinese NER either do not exploit word boundary information from CWS or cannot filter the specific information of CWS. In this paper, we propose a novel adversarial transfer learning framework to make full use of task-shared boundaries information and prevent the task-specific features of CWS. Besides, since arbitrary character can provide important cues when predicting entity type, we exploit self-attention to explicitly capture long range dependencies between two tokens. Experimental results on two different widely used datasets show that our proposed model significantly and consistently outperforms other state-of-the-art methods.
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 | SighanNER | BiLSTM+CRF+adversarial+self-attention | F1 | 90.64 | #1 of 1 | Archive leaderboard | report |
| Chinese Named Entity Recognition | Weibo NER | BiLSTM+CRF+adversarial+self-attention | F1 | 53.08 | #17 of 18 | 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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