Generates skipgram word pairs.
skipgrams(
sequence,
vocabulary_size,
window_size = 4,
negative_samples = 1,
shuffle = TRUE,
categorical = FALSE,
sampling_table = NULL,
seed = NULL
)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.
Int, maximum possible word index + 1
Int, size of sampling windows (technically half-window).
The window of a word w_i will be [i-window_size, i+window_size+1]
float >= 0. 0 for no negative (i.e. random) samples. 1 for same number as positive samples.
whether to shuffle the word couples before returning them.
bool. if FALSE, labels will be integers (eg. [0, 1, 1 .. ]),
if TRUE labels will be categorical eg. [[1,0],[0,1],[0,1] .. ]
1D array of size vocabulary_size where the entry i
encodes the probabibily to sample a word of rank i.
Random seed
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].
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
Other text preprocessing:
make_sampling_table(),
pad_sequences(),
text_hashing_trick(),
text_one_hot(),
text_to_word_sequence()