Semantic analysis is a fundamental aspect of Natural Language Processing (NLP) that focuses on understanding the meaning of words and their relationships. This chapter explores key concepts in semantics, including semantic grammar, lexical semantics, lexemes, and word senses. Various word relationships, such as hyponymy, homonymy, polysemy, synonymy, and antonym, are examined to illustrate their role in language comprehension. WordNet, a lexical database, is discussed for its application in word similarity and semantic analysis. Additionally, Word Sense Disambiguation (WSD), a crucial technique for resolving word meaning in different contexts, is explored through dictionary-based approaches. The significance of word similarity measures and their impact on NLP tasks like information retrieval is also highlighted. By analyzing these fundamental semantic concepts, this study provides insights into improving machine understanding of natural language, enhancing applications such as search engines, text classification, and automated language translation.