vectorize.words: Word vectorization

In fdm2id: Data Mining and R Programming for Beginners

Word vectorization

Description

Vectorize words from a corpus of documents.

Usage

vectorize.words(
  corpus = NULL,
  ndim = 50,
  maxwords = NULL,
  mincount = 5,
  minphrasecount = NULL,
  window = 5,
  maxcooc = 10,
  maxiter = 10,
  epsilon = 0.01,
  lang = "en",
  stopwords = lang,
  excludewords = NULL,
  removesinglechars = TRUE,
  ...
)

Arguments

corpus	The corpus of documents (a vector of characters).
ndim	The number of dimensions of the vector space.
maxwords	The maximum number of words.
mincount	Minimum word count to be considered as frequent.
minphrasecount	Minimum collocation of words count to be considered as frequent.
window	Window for term-co-occurrence matrix construction.
maxcooc	Maximum number of co-occurrences to use in the weighting function.
maxiter	The maximum number of iteration to fit the GloVe model.
epsilon	Defines early stopping strategy when fit the GloVe model.
lang	The language of the documents (NULL if no stemming).
stopwords	The language whose stop words are removed ("en", ...), or NULL to keep them. A list of words of your own goes to excludewords.
excludewords	An optional custom vector of additional words to exclude from the vocabulary (e.g. corpus-specific stop words), on top of (or instead of) the language stopwords given through stopwords.
removesinglechars	Whether single-character tokens are removed during cleanup.
...	Other parameters.

Value

The vectorized words.

See Also

query.words, stopwords, vectorizers

Examples

# 'capitals' is small, so the word vectors are coarse and 'ndim' is reduced
# accordingly; phrase detection needs a much larger corpus.
data (capitals)
words = vectorize.words (capitals, mincount = 2, ndim = 10, maxiter = 5)
query.words (words, origin = "paris", sub = "france", add = "germany")
query.words (words, origin = "berlin", sub = "germany", add = "france")

fdm2id documentation built on Aug. 28, 2026, 9:07 a.m.