Fusing Transformer-Based Contextual Embeddings with Structured Semantic Knowledge for Improved Word Sense Disambiguation in Natural Language Processing

Abstract

Word Sense Disambiguation (WSD) is a critical task in Natural Language Processing (NLP) that aims to determine the intended meaning of ambiguous words based on their contextual usage. Although Transformer-based language models have significantly improved contextual understanding, their decision-making process often lacks semantic transparency and explainability. Conversely, knowledge-based approaches leverage lexical databases and ontological resources to provide interpretable semantic relationships but are constrained by limited adaptability to diverse linguistic contexts. This research introduces a hybrid WSD framework that combines contextual representations generated by Transformer architectures with structured semantic knowledge extracted from ontology-driven repositories. By integrating contextual embeddings with knowledge embeddings, the proposed model enhances both contextual sensitivity and semantic consistency during the sense prediction process. The knowledge infusion mechanism enables the model to validate contextual interpretations using explicit semantic relationships, thereby improving the reliability of word sense assignments. Experimental evaluation demonstrates that the proposed framework achieves superior performance compared with standalone contextual and knowledge-based approaches, yielding statistically significant improvements in precision, recall, and F1-score. The results indicate that combining deep contextual learning with structured semantic knowledge provides a robust and interpretable solution for accurate word sense disambiguation across diverse linguistic scenarios