This post categorized under Vector and posted on August 1st, 2018.

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Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers.A Word Embedding format generally tries to map a word using a dictionary to a vector. Let us break this sentence down into finer details to have a clear view. Let us break this sentence down into finer details to have a clear view.Why is Distributed Representation of Word Important In this section I will explain the importance of distributed representation of words in NLP. First I

The vectors created by Word Embedding preserve these similarities so words that regularly occur nearby in text will also be in close proximity in vector graphice. For examples of why this is useful check out The amazing power of word vectors or this intro to Distributed Word Vectors on Kaggle.Do word vectors obtained via word embedding techniques really form a vector graphice Is it even practical to concern about this machine-learning natural-language feature-construction word2vec word-embeddingsNaturally every feed-forward neural network that takes words from a vocabulary as input and embeds them as vectors into a lower dimensional graphice which it then fine-tunes through back-propagation necessarily yields word embeddings as the weights of the first layer which is usually referred to as Embedding Layer.

Predictive models directly try to predict a word from its neighbors in terms of learned small dense embedding vectors (considered parameters of the model). Word2vec is a particularly computationally-efficient predictive model for learning word Using word vector representations and embedding layers you can train recurrent neural networks with outstanding performances in a wide variety of industries. Examples of applications are sentiment graphicysis named engraphicy recognition and machine translation.