Create a doc2vec model — as.textmodel

Create a doc2vec model as weighted word vectors.

Usage

as.textmodel_doc2vec(x, model, compound = TRUE, group_data = FALSE, ...)

Arguments

x: a quanteda::tokens or quanteda::dfm object.
model: a textmodel_wordvector object.
compound: if TRUE, compound multi-word expressions in x based on model internally. Only applies when x is a quanteda::tokens object.
group_data: if TRUE, apply dfm_group(x) before creating document vectors.
...: additional arguments passed to the underlying function.

Value

Returns a textmodel_doc2vec object with the following elements:

values: a list of matrices for word and document vectors.
dim: the size of the document vectors.
concatenator: the concatenator in x.
docvars: document variables copied from x.
call: the command used to execute the function.
version: the version of the wordvector package.

Details

For Japanese or Chinese texts, model$concatenator must be empty (""). The value is inherited from the tokens object on which the model was trained. It triggers tokenization of words in model and compounding of characters in x before creating document vectors.