fem {conText}

Create an feature-embedding matrix

Description

Given a featureco-occurrence matrix for each feature, multiply its feature counts (columns) with their corresponding pre-trained embeddings and average (usually referred to as averaged or additive embeddings). If specified and a transformation matrix is provided, multiply the feature embeddings by the transformation matrix to obtain the corresponding ⁠a la carte⁠ embeddings. (see eq 2: https://arxiv.org/pdf/1805.05388.pdf)

Usage

fem(x, pre_trained, transform = TRUE, transform_matrix, verbose = TRUE)

Arguments

x

a quanteda (fcm-class) feature-co-occurrence-matrix

pre_trained

(numeric) a F x D matrix corresponding to pretrained embeddings. F = number of features and D = embedding dimensions. rownames(pre_trained) = set of features for which there is a pre-trained embedding.

transform

(logical) if TRUE (default) apply the 'a la carte' transformation, if FALSE ouput untransformed averaged embeddings.

transform_matrix

(numeric) a D x D 'a la carte' transformation matrix. D = dimensions of pretrained embeddings.

verbose

(logical) - if TRUE, report the features that had no overlapping (co-occurring) features with the pretrained embeddings provided.

Value

a fem-class object

Examples

library(quanteda)

# tokenize corpus
toks <- tokens(cr_sample_corpus)

# create feature co-occurrence matrix (set tri = FALSE to work with fem)
toks_fcm <- fcm(toks, context = "window", window = 6,
count = "frequency", tri = FALSE)

# compute feature-embedding matrix
toks_fem <- fem(toks_fcm, pre_trained = cr_glove_subset,
transform = TRUE, transform_matrix = cr_transform, verbose = FALSE)