skipgrams {keras}

Generates skipgram word pairs.

Description

Generates skipgram word pairs.

Usage

skipgrams(
  sequence,
  vocabulary_size,
  window_size = 4,
  negative_samples = 1,
  shuffle = TRUE,
  categorical = FALSE,
  sampling_table = NULL,
  seed = NULL
)

Arguments

sequence

A word sequence (sentence), encoded as a list of word indices (integers). If using a sampling_table, word indices are expected to match the rank of the words in a reference dataset (e.g. 10 would encode the 10-th most frequently occuring token). Note that index 0 is expected to be a non-word and will be skipped.

vocabulary_size

Int, maximum possible word index + 1

window_size

Int, size of sampling windows (technically half-window). The window of a word w_i will be ⁠[i-window_size, i+window_size+1]⁠

negative_samples

float >= 0. 0 for no negative (i.e. random) samples. 1 for same number as positive samples.

shuffle

whether to shuffle the word couples before returning them.

categorical

bool. if FALSE, labels will be integers (eg. ⁠[0, 1, 1 .. ]⁠), if TRUE labels will be categorical eg. ⁠[[1,0],[0,1],[0,1] .. ]⁠

sampling_table

1D array of size vocabulary_size where the entry i encodes the probabibily to sample a word of rank i.

seed

Random seed

Details

This function transforms a list of word indexes (lists of integers) into lists of words of the form:

• (word, word in the same window), with label 1 (positive samples).

• (word, random word from the vocabulary), with label 0 (negative samples).

Read more about Skipgram in this gnomic paper by Mikolov et al.: Efficient Estimation of Word Representations in Vector Space

Value

List of couples, labels where:

• couples is a list of 2-element integer vectors: ⁠[word_index, other_word_index]⁠.

• labels is an integer vector of 0 and 1, where 1 indicates that other_word_index was found in the same window as word_index, and 0 indicates that other_word_index was random.

• if categorical is set to TRUE, the labels are categorical, ie. 1 becomes ⁠[0,1]⁠, and 0 becomes ⁠[1, 0]⁠.

See Also

Other text preprocessing: make_sampling_table(), pad_sequences(), text_hashing_trick(), text_one_hot(), text_to_word_sequence()