• Word Embedding is a form of word representation that bridges the human understanding of language to that of a machine.
• These are found to be useful representations of words and lead to better performance in the various tasks performed.

Machine learning models cannot process text so we need to convert this textual data into numerical data. Hence, we have to use this algorithm.

When required to represent words or phrases in vector space with several dimensions

In applications where antonyms or even synonyms are required to be used.

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One textual column

Vector norm – Higher the frequency of the word, larger is the value of the norm in this word embedding.

None

Words with multiple meanings are often combined into a single representation.