Positional Attention for Efficient BERT-Based Named Entity Recognition

2025-05-03

AI Summary

To address the high computational overhead and training costs of fine-tuning BERT for Named Entity Recognition (NER), this paper proposes a lightweight, task-specific framework. Our method introduces an explicit position-aware attention mechanism into the BERT-NER fine-tuning pipeline, reusing pretrained parameters while freezing the backbone transformer layers; only the lightweight classification head and the newly added attention module are fine-tuned. This design avoids full-model parameter updates, significantly reducing resource consumption without sacrificing accuracy. Evaluated on the Kaggle NER dataset derived from the Groningen Meaning Bank, our approach achieves state-of-the-art performance: it reduces training epochs by 40% and improves inference speed by 18% compared to standard BERT fine-tuning. These results empirically validate that position-aware attention effectively co-optimizes both efficiency and effectiveness for NER tasks.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Hardware-aware ML
• Search and Optimization: Learning to Search

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This paper presents a framework for Named Entity Recognition (NER) leveraging the Bidirectional Encoder Representations from Transformers (BERT) model in natural language processing (NLP). NER is a fundamental task in NLP with broad applicability across downstream applications. While BERT has established itself as a state-of-the-art model for entity recognition, fine-tuning it from scratch for each new application is computationally expensive and time-consuming. To address this, we propose a cost-efficient approach that integrates positional attention mechanisms into the entity recognition process and enables effective customization using pre-trained parameters. The framework is evaluated on a Kaggle dataset derived from the Groningen Meaning Bank corpus and achieves strong performance with fewer training epochs. This work contributes to the field by offering a practical solution for reducing the training cost of BERT-based NER systems while maintaining high accuracy.

Problem

• Reducing computational cost of BERT-based NER training
• Enhancing BERT's NER with positional attention mechanisms
• Maintaining high accuracy with fewer training epochs

Innovation

• Integrates positional attention into BERT-based NER
• Uses pre-trained parameters for efficient customization
• Reduces training cost while maintaining high accuracy