compute_transform {conText} | R Documentation |
Compute transformation matrix A
Description
Computes a transformation matrix, given a feature-co-occurrence matrix and corresponding pre-trained embeddings.
Usage
compute_transform(x, pre_trained, weighting = 500)
Arguments
x |
a (quanteda) |
pre_trained |
(numeric) a F x D matrix corresponding to pretrained embeddings,
usually trained on the same corpus as that used for |
weighting |
(character or numeric) weighting options:
Recommended: use |
Value
a dgTMatrix-class
D x D non-symmetrical matrix
(D = dimensions of pre-trained embedding space) corresponding
to an 'a la carte' transformation matrix. This matrix is optimized
for the corpus and pre-trained embeddings employed.
Examples
library(quanteda)
# note, cr_sample_corpus is too small to produce sensical word vectors
# tokenize
toks <- tokens(cr_sample_corpus)
# construct feature-co-occurrence matrix
toks_fcm <- fcm(toks, context = "window", window = 6,
count = "weighted", weights = 1 / (1:6), tri = FALSE)
# you will generally want to estimate a new (corpus-specific)
# GloVe model, we will use cr_glove_subset instead
# see the Quick Start Guide to see a full example.
# estimate transform
local_transform <- compute_transform(x = toks_fcm,
pre_trained = cr_glove_subset, weighting = 'log')