A Novel Mechanism for Resolving Word Sense Disambiguation in Natural Language Processing

Abstract

Word Sense Disambiguation (WSD) is a fundamental challenge in Natural Language Processing (NLP), crucial for tasks such as machine translation, sentiment analysis, and information retrieval. Ambiguity in word meanings often leads to misinterpretation, affecting the accuracy of language models and automated text-processing systems. This paper presents a novel mechanism for resolving Word Sense Disambiguation, integrating both knowledge-based and machine learning approaches to enhance contextual understanding.

The proposed method leverages semantic networks and contextual embeddings, utilizing WordNet for lexical knowledge and transformer-based deep learning models for contextual analysis. By combining rule-based heuristics with data-driven learning, our approach improves sense identification while maintaining computational efficiency.

The methodology involves preprocessing text, extracting contextual features, applying a hybrid disambiguation model, and evaluating performance using benchmark datasets such as SemCor and Senseval. Performance evaluation, based on precision, recall, and F1-score, demonstrates that our approach outperforms traditional WSD techniques, achieving improved accuracy in distinguishing word meanings across diverse contexts.

The results indicate that the proposed mechanism enhances WSD efficiency, making it a viable solution for NLP applications requiring high semantic accuracy. Future research will explore integrating domain-specific knowledge bases and real-time applications to further refine the disambiguation process.