AI News, BOOK REVIEW: Word Embeddings: A Natural Language Processing Crash Course

Word Embeddings: A Natural Language Processing Crash Course

In a typical bag-of-words model, each word is considered a unique token with no relationship to other words.

For example, the words 'salt' and 'seasoning' willbe assigned unique IDs even though they may frequently appear within the same context or sentence.

Word embedding is a set of feature engineering techniques that map sparse word vectors into continuous space based on the surrounding context.

Word2vec isa predictive model, which means that instead of utilizing word counts à la latent Dirichlet allocation (LDA),it is trained to predict a target word from the context of its neighboring words.

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