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Understanding the meaning of a word has always been a challenging task for machines. There are circumstances when even an easy word becomes difficult to understand. This understanding is not limited to its meaning but other criteria like identifying similar words, solving ambiguity, co reference resolution, etc. Semantic features and semantic relations can be identified as the building blocks for the semantic illustration of a concept. Understanding a language by machine requires understanding of words both linguistically and computationally. This paper tries to critically review the semantic features and relations created with their extraction methods.