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This chapter focuses on semantic analysis, a fundamental concept in learning NLP. It begins by introducing two primary approaches to semantic analysis: lexical semantics and compositional semantics. The discussion then extends to word senses and six commonly used lexical semantic techniques, followed by an exploration of word sense disambiguation (WSD) and various WSD methodologies. Additionally, the chapter examines WordNet and online thesauri for word similarity, as well as distributed similarity measures, including Pointwise Mutual Information (PMI) and Positive Pointwise Mutual Information (PPMI) models, illustrated with live examples. Chapters 4 and 5 also provide the conceptual groundwork for Workshop #4: Semantic Analysis and Word Vectors using spaCy, presented in Chap. 14 .

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Semantic Analysis

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  • 发表日期：

    2025年

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  • Raymond Lee

摘要

This chapter focuses on semantic analysis, a fundamental concept in learning NLP. It begins by introducing two primary approaches to semantic analysis: lexical semantics and compositional semantics. The discussion then extends to word senses and six commonly used lexical semantic techniques, followed by an exploration of word sense disambiguation (WSD) and various WSD methodologies. Additionally, the chapter examines WordNet and online thesauri for word similarity, as well as distributed similarity measures, including Pointwise Mutual Information (PMI) and Positive Pointwise Mutual Information (PPMI) models, illustrated with live examples. Chapters 4 and 5 also provide the conceptual groundwork for Workshop #4: Semantic Analysis and Word Vectors using spaCy, presented in Chap. 14 .