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vec2word

Map embedding vector to word

Description

words = vec2word(emb,M) returns the closest words to the embedding vectors in the rows of M.

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[words,dist] = vec2word(emb,M) returns the closest words to the embedding vectors in M, and returns the distances dist of each to their source vectors.

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___ = vec2word(emb,M,k) returns the top k closest words.

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___ = vec2word(___,'Distance',distance) specifies the distance metric.

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Examples

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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 = 
"France"

Find the top five closest words to a word embedding vector and their distances.

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;

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. Find the top five closest words using the Euclidean distance metric.

k = 5;
M = italy - rome + paris;
[words,dist] = vec2word(emb,M,k,'Distance','euclidean');

Plot the words and distances in a bar chart.

figure;
bar(dist)
xticklabels(words)
xlabel("Word")
ylabel("Distance")
title("Distances to Vector")

Input Arguments

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Input word embedding, specified as a wordEmbedding object.

Word embedding vectors, specified as a matrix. Each row of M is a word embedding vector. M must have emb.Dimension columns.

Number of closest words to return, specified as a positive integer.

Distance metric, specified as 'cosine' or 'euclidean'.

Output Arguments

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Output words, returned as a string vector.

Distance of words to their source vectors, returned as a vector.

Version History

Introduced in R2017b