A Comprehensive Review Of Word Sense Disambiguation For Enhancing Machine Translation Systems

Abstract

Purpose: This paper provides a comprehensive review of Word Sense Disambiguation (WSD) and its critical role in enhancing the accuracy and quality of machine translation (MT) systems. The core objective is to analyze the foundational concepts, established techniques, and practical applications of WSD, with a specific focus on its impact on lexical ambiguity in natural language translation.

Design/Methodology/Approach: The article employs a systematic review methodology, synthesizing key literature on WSD and MT from foundational works to recent research. The review explores a theoretical framework for WSD, examining both knowledge-based and data-driven algorithms. It emphasizes the importance of lexical resources, such as WordNet, as the "heart of NLP," and their direct application in improving translation performance.

Findings: The review confirms that WSD is a fundamental and successful technique for overcoming lexical ambiguity, a major challenge in MT. The analysis of case studies and performance metrics shows that the integration of WSD significantly improves the coherence and contextual accuracy of translated text, particularly for polysemous words, idioms, and postpositions.

Originality/Value: This paper provides a consolidated and focused review that highlights the direct link between WSD and "true translation" quality, a topic not comprehensively addressed in a single, dedicated review. By synthesizing a range of sources, it offers a valuable resource for researchers and practitioners in NLP and MT, outlining current challenges and proposing future research directions.