In the preceding section, we studied how we can perform NLP by using BoW as the abstraction for the input text data. One of the major advancements in NLP is our ability to create a meaningful numeric representation of words in the form of dense vectors. This technique is called word embedding. Yoshua Bengio first introduced the term in his paper A Neural Probabilistic Language Model. Each word in an NLP problem can be thought of as a categorical object. Mapping each of the words to a list of numbers represented as a vector is called word embedding. In other words, the methodologies that are used to convert words into real numbers are called word embedding. A differentiating feature of embedding is that it uses a dense vector, instead of using traditional approaches that use sparse matrix vectors.
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