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Asked by: Tehmine Idieder
technology and computing artificial intelligenceWhat is word vector in NLP?
Accordingly, what is word Embeddings in NLP?
Word embeddings are basically a form of word representation that bridges the human understanding of language to that of a machine. Word embeddings are distributed representations of text in an n-dimensional space. These are essential for solving most NLP problems.
Likewise, people ask, how do you represent a word as a vector?
Words are represented by dense vectors where a vector represents the projection of the word into a continuous vector space. It is an improvement over more the traditional bag-of-word model encoding schemes where large sparse vectors were used to represent each word.
Word Embedding aims to create a vector representation with a much lower dimensional space. Word Embedding is used for semantic parsing, to extract meaning from text to enable natural language understanding.