Enhanced NER Tagging Model using Relative Positional Encoding Transformer Model - Achir, Jerome Aondongu; Abdulkarim, Muhammed; Abdullahi , Mohammed (yocto brain, 2025)
Named Entity Recognition remains pivotal for structuring unstructured text, yet existing models face challenges with long-range dependencies, domain generalisation, and reliance on large, annotated datasets. To address these limitations, this paper introduces a hybrid architecture combining a transformer model enhanced with relative positional encoding and a rule-based refinement module. Relative positional encoding improves contextual understanding by capturing token relationships dynamically, while rule-based post-processing corrects inconsistencies in entity tagging. After being evaluated on the Groningen Meaning Bank and Wikipedia Location datasets, the proposed model achieves state-of-the-art performance, with validation accuracies of 98.91% for Wikipedia and 98.50% for GMB with rule-based refinement, surpassing existing benchmark research of 94.0%. The relative positional encoding contributes 34.42% to the attention mechanism’s magnitude, underscoring its efficacy in modelling token interactions. Results demonstrate that integrating transformer-based architectures with rule-based corrections significantly enhances entity classification accuracy, particularly in complex and morphologically diverse contexts. This work highlights the potential of hybrid approaches to optimise sequence labelling tasks across domains.