用迭代自标注提升科学实体识别准确率，尤其对少见实体效果显著。
Iterative Auto-Annotation for Scientific Named Entity Recognition Using BERT-Based Models
- 先用少量人工标注数据微调BERT模型，再用模型自动标注更多数据并迭代优化。
- 在少样本实体上F1得分提升明显，bert-large-cased模型表现优于其他版本。
- 适合标注数据稀缺的科研文本处理场景，可推广至其他NLP任务。
本文提出一种基于BERT模型的科学命名实体识别（SciNER）迭代自标注方法。利用迁移学习，以少量高质量人工标注数据微调预训练模型，并通过模型自动标注更大规模数据集，再进行多轮微调迭代优化。实验对比了dslim/bert-large-NER与bert-large-cased两个模型，结果表明bert-large-cased在各项指标上持续领先。该方法显著提升了预测准确率和F1分数，尤其在低频实体类别上改善明显。未来工作可考虑无标签数据的领域适应、探索RoBERTa等更强编码器，以及扩大人工标注范围。该方法在标注数据受限的NLP任务中具有广泛适用性。
原文摘要 · Abstract (English)
This paper presents an iterative approach to performing Scientific Named Entity Recognition (SciNER) using BERT-based models. We leverage transfer learning to fine-tune pretrained models with a small but high-quality set of manually annotated data. The process is iteratively refined by using the fine-tuned model to auto-annotate a larger dataset, followed by additional rounds of fine-tuning. We evaluated two models, dslim/bert-large-NER and bert-largecased, and found that bert-large-cased consistently outperformed the former. Our approach demonstrated significant improvements in prediction accuracy and F1 scores, especially for less common entity classes. Future work could include pertaining with unlabeled data, exploring more powerful encoders like RoBERTa, and expanding the scope of manual annotations. This methodology has broader applications in NLP tasks where access to labeled data is limited.
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