Word Embedding

Word Embedding

 Word Embedding

Description

  • 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.

Why to use

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 to use

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

When not to use

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

Prerequisites

-

Input

One textual column

Output

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

Statistical Methods Used

None

Limitations

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



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