corshrink_word2vec: Adaptive Shrinkage of cosine similarities in word2vec output.
In kkdey/WEAVER: Word Enrichment Analysis using Vector Representations

Performs an adaptive shrinkage (ash) of the cosine similarities in word2vec output. The ash framework has been proposed by Matthew Stephens (2016).

1 2	corshrink_word2vec(model_true, model_boot_list, word_vec, num_related_words = 500)

`model_true`	A word2vec VectorSpace model output obtained by fitting the model on corpus.
`model_boot_list`	A list of VectorSpace models obtained by fitting the word2vec on resampled corpus data.
`word_vec`	A word set or a vector of words of interest. Shrinkage of cosine similarities for the primary linked words to each word in this word set.
`num_related_words`	The number of primary linked words to each word taken into the model.

Returns a list with each element corresponding to ash results for each word in word_vec. An element of the list is anopther list containing the following features. similar_words : all the words taken into the shrinkage model for each word in word_vec. cosine_est: cosine similarities from the original word2vec model sd_cosine_transform_est: standard error of Fisher z-scores obtained from cosine similarities of primary links with each word in word_vec from resampled corpus data. ash_out: The ash result for each word in word_vec ash_cosine_est: ash shrunk cosine similarities for each word in word_vec.