Enhancing Financial Named Entity Recognition through Adaptive Few-Shot Learning: A Comparative Study of Pre-trained Language Models
Ziyi Wang · Journal of Advanced Computing Systems · 2024
Financial document processing faces significant challenges in extracting structured information from diverse document types including loan applications, financial statements, and regulatory filings. This paper presents an adaptive few-shot learning framework for Named Entity Recognition (NER) in financial documents, addressing the critical need to reduce annotation requirements while maintaining high extraction accuracy. We conduct a comprehensive comparative analysis of pre-trained language models including BERT, RoBERTa, and domain-specific FinBERT variants under few-shot learning scenarios. Our methodology integrates meta-learning approaches with prompt-based optimization strategies, enabling effective entity recognition with minimal labeled examples. Experimental results on financial document datasets demonstrate that our adaptive framework achieves 91.3% F1-score with only 10 labeled examples per entity type, representing a 68% reduction in annotation requirements compared to traditional supervised approaches. The proposed approach significantly benefits financial institutions by reducing manual processing costs while maintaining regulatory compliance standards.