as.textmodel_lss: Create a Latent Semantic Scaling model from various objects

In LSX: Semi-Supervised Algorithm for Document Scaling

Create a Latent Semantic Scaling model from various objects

Description

Create a new textmodel_lss object from an existing or foreign objects.

Usage

as.textmodel_lss(x, ...)

Arguments

x	an object from which a new textmodel_lss object is created. See details.
...	arguments used to create a new object. seeds must be given when x is a dense matrix or a fitted textmodel_lss.

Details

If x is a textmodel_lss, original word vectors are reused to compute polarity scores with new seed words. It is also possible to subset word vectors via slice if it was trained originally using SVD.

If x is a dense matrix, it is treated as a column-oriented word vectors with which polarity of words are computed. If x is a named numeric vector, the values are treated as polarity scores of the words in the names.

If x is a normalized wordvector::textmodel_word2vec, it returns a spatial model; if not normalized, a probabilistic model. While the polarity scores of words are their cosine similarity to seed words in spatial models, they are predicted probability that the seed words to occur in their proximity.

Value

a dummy textmodel_lss object

LSX documentation built on June 20, 2025, 1:08 a.m.