BERT-Based Word Sense Disambiguation with Multi-Scale Feature Fusion

Word Sense Disambiguation (WSD) is a fundamental task in Natural Language Processing, challenged by the presence of polysemous words in context. This paper proposes a novel WSD model, MCGF-BERT, which integrates BERT-based contextual embeddings with multi-scale convolutional features and an adaptive gating mechanism. BERT encodes rich semantic context, while parallel convolutional layers capture local features at varying granularities. A lightweight gating unit fuses these features and enhances discriminative information for sense disambiguation. We first conduct experiments on a manually annotated Chinese WSD dataset containing 40 ambiguous target words, where our model achieves an accuracy of 85.04%, outperforming BiLSTM (77.12%) and standard BERT (81.37%). To assess the model's generalizability, we extend the evaluation to two standard English benchmarks: SemCor and ALL-WSD. On SemCor, our model achieves 75.3% accuracy, surpassing GlossBERT (73.2%) and BEM (72.8%). On ALL-WSD, MCGFBERT reaches 74.1%, which demonstrates consistent advantages across benchmarks. Ablation studies further verify the contributions of the multi-scale fusion and the gating mechanism, with performance drops of 2.6% and 1.9% when these modules are removed, respectively. While our model is designed with computational efficiency in mind, detailed profiling of parameter overhead and inference latency is left for future work.