Map word to embedding vector



M = word2vec(emb,words) returns the embedding vectors of words in the embedding emb. If a word is not in the embedding vocabulary, then the function returns a row of NaNs.


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Load a pretrained word embedding using fastTextWordEmbedding. This function requires Text Analytics Toolbox™ Model for fastText English 16 Billion Token Word Embedding support package. If this support package is not installed, then the function provides a download link.

emb = fastTextWordEmbedding
emb = 
  wordEmbedding with properties:

     Dimension: 300
    Vocabulary: [1×1000000 string]

Map the words "Italy", "Rome", and "Paris" to vectors using word2vec.

italy = word2vec(emb,"Italy");
rome = word2vec(emb,"Rome");
paris = word2vec(emb,"Paris");

Map the vector italy - rome + paris to a word using vec2word.

word = vec2word(emb,italy - rome + paris)
word = 

Input Arguments

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Input word embedding, specified as a wordEmbedding object.

Input words, specified as a string vector, character vector, or cell array of character vectors. If you specify words as a character vector, then the function treats the argument as a single word.

Data Types: string | char | cell

Output Arguments

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Matrix of word embedding vectors.

Introduced in R2017b