Iterative Auto-Annotation for Scientific Named Entity Recognition Using BERT-Based Models
This is an incremental method for NLP researchers and practitioners dealing with data scarcity in scientific text analysis.
The paper tackled the problem of limited labeled data for Scientific Named Entity Recognition by proposing an iterative auto-annotation approach using BERT-based models, resulting in significant improvements in prediction accuracy and F1 scores, particularly for less common entity classes.
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.