How deeply does Deep Learning with R, Third Edition cover Word2Vec?

Explains the embedding principle, similarity measure, emergent relationships, and contrast with Transformers without detailing training implementation.

Word2Vec

As explained in Deep Learning with R, Third Edition

word2vec is a family of shallow neural models that learn vector representations of words from local context relationships.

Word2Vec is a pretrained embedding that converts word relationships into distances in a vector space. Words that frequently co-occur receive vectors with high similarity, while rarely co-occurring words receive vectors with low similarity. section 15.5

Word2Vec is notable for several properties:

It assigns one vector to each vocabulary word and uses cosine proximity to compare them. section 15.5
Its embedding space can capture semantic similarity and emergent relationships not explicitly included in training. section 15.5
Unlike a Transformer, it cannot generate fluent language, but both models place tokens near one another when they appear together. section 15.5

Word arithmetic

Vector operations can express relationships such as V(king) − V(man) + V(woman) ≈ V(queen). Similar directions may represent plurality or the relationship between wild animals and pets. section 15.5

How Deep Learning with R, Third Edition covers it

Cover of Deep Learning with R, Third Edition

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Also explained in

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Natural Language Processing in Action, Second EditionWord2Vec is a family of neural language models that learns dense vector representations of words from nearby-word prediction in text.
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Data Without LabelsWord2Vec is a family of neural language models that learns vector representations of words from their surrounding context in a text corpus.

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Related concepts

embeddingproduces · A numerical vector representation of an item or input that encodes features or relationships useful for computational tasks.
GloVecontrasts with · GloVe is a word-embedding method that learns vector representations from global word-co-occurrence statistics across a corpus.
skip-gramuses · Skip-gram is a word-embedding training architecture that learns to predict surrounding context items from a target item.
backpropagationuses · Backpropagation is a procedure for computing derivatives of an output or loss with respect to inputs and parameters by applying the chain rule backward through a computational graph.
distributional hypothesisimplements · The distributional hypothesis is the linguistic principle that words or phrases appearing in similar contexts tend to have related meanings.
gensimused by · Gensim is an open-source Python library for unsupervised natural-language processing, including topic modeling, document similarity, and distributed representations.

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with R, Third Edition. The section links point to the parts of the book it draws on.