Word2Vec

As explained in Data Without Labels

Word2Vec is a family of neural language models that learns vector representations of words from their surrounding context in a text corpus.

Word2Vec is a family of prediction-based models that learns compact vector representations of words from a large text corpus. Words appearing in similar contexts receive nearby vectors, allowing the representation to capture semantic relationships more effectively than one-hot encoding or bag-of-words. section 7.13 section 7.14

Word2Vec uses two related learning objectives:

• Continuous bag of words (CBOW) predicts the current target word from a surrounding context window. section 7.14
• Continuous skip-gram uses the current word to predict surrounding context words, weighting neighboring words more heavily than distant words. section 7.14

The learned vectors encode relationships through co-occurrence: words that frequently share contexts become close in the vector space. Word2Vec is prediction-based, whereas GloVe is frequency-based. The underlying mathematics is deferred, but Python code can be used to develop the models. section 7.14 section 7.13

Relational word vectors

Vector relationships can represent analogies such as men-to-women and king-to-queen, where both express a male-to-female relationship. section 7.13

Figure 7.16 Word embeddings can be used to represent the relationships between words. For example, there is a relation from “men” to “women” that is similar to “king” to “queen” as both “men-women” and “king-queen” represent the male-female gender relationship.

Use the relationship diagram to inspect how word embeddings place analogous word pairs in relation to one another. section 7.13

How Data Without Labels covers it

How deep it goes

Explains embeddings, co-occurrence, CBOW, and skip-gram, then defers the mathematical foundation while supporting Python implementation.

Examples

Uses the men–women and king–queen analogy to illustrate relational structure in vector space.

What it assumes

Assumes text-corpus data and basic representation methods while introducing contextual word vectors from their working mechanism.

What you can do after

Evaluate/understand contextual word representations and develop Word2Vec models with Python.

How it connects

Connects word embeddings with co-occurrence, CBOW, continuous skip-gram, GloVe, bag-of-words, and one-hot encoding.

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Also explained in

• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  Deep Learning with R, Third Editionword2vec is a family of shallow neural models that learn vector representations of words from local context relationships.

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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 Data Without Labels. The section links point to the parts of the book it draws on.