Analysis of Semantic Disambiguation Techniques in Dialogue Systems
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Keywords
human-computer dialogue systems, artificial intelligence, word sense disambiguation, natural language processing
Abstract
With the rapid advancement of Artificial Intelligence (AI), human-computer dialogue systems have emerged as a prominent application in the AI field, where semantic disambiguation plays a critical role. This paper provides a comprehensive survey and analysis of semantic disambiguation techniques in Natural Language Processing. It not only examines the strengths and limitations of traditional approaches but also investigates state-of-the-art methods that are shaping the field. Through a systematic review, this study outlines the overall development trajectory of disambiguation technologies and identifies key challenges that remain unresolved. While significant breakthroughs have been achieved in semantic disambiguation, this review reveals that substantial challenges persist in areas such as contextual understanding, cross-domain adaptation, and real-time processing. Overall, this paper emphasizes the growing importance of integrating knowledge-driven and data-driven approaches, highlighting the potential of hybrid models that combine linguistic rules, contextual embeddings, and large language models. The findings aim to offer valuable insights for future research directions and practical implementations in evolving dialogue systems.
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