Package 'wordvector'

Convert formula to named character vector

Description

Convert a formula to a named character vector in analogy tasks.

Usage

analogy(formula)

Arguments

formula

a formula object that defines the relationship between words using + or - operators.

Value

a named character vector to be passed to similarity().

See Also

similarity()

Examples

analogy(~ berlin - germany + france)
analogy(~ quick - quickly + slowly)

---

Extract word vectors

Description

Extract word vectors from a textmodel_wordvector or textmodel_docvector object.

Usage

## S3 method for class 'textmodel_wordvector'
as.matrix(x, ...)

Arguments

x	a textmodel_wordvector or textmodel_docvector object.
...	not used

Value

a matrix that contain the word vectors in rows

---

Yahoo News summaries from 2014

Description

A corpus object containing 2,000 news summaries collected from Yahoo News via RSS feeds in 2014. The title and description of the summaries are concatenated.

Usage

data_corpus_news2014

Format

An object of class corpus (inherits from character) of length 20000.

Source

https://www.yahoo.com/news/

References

Watanabe, K. (2018). Newsmap: A semi-supervised approach to geographical news classification. Digital Journalism, 6(3), 294–309. https://doi.org/10.1080/21670811.2017.1293487

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Compute similarity between word vectors

Description

Compute cosine similarity between word vectors for selected words.

Usage

similarity(x, words, mode = c("words", "values"))

Arguments

x	a textmodel_wordvector object.
words	words for which similarity is computed.
mode	specify the type of resulting object.

Value

a matrix of cosine similarity scores when mode = "values" or of words sorted in descending order by the similarity scores when mode = "words". When words is a named numeric vector, word vectors are weighted and summed before computing similarity scores.

See Also

analogy()

---

Create distributed representation of documents

Description

Create distributed representation of documents as weighted word vectors.

Usage

textmodel_doc2vec(x, model = NULL, ...)

Arguments

x	a quanteda::tokens object.
model	a textmodel_wordvector object.
...	passed to ⁠[word2vec]⁠ when model = NULL.

Value

Returns a textmodel_docvector object with elements inherited from model or passed via ... plus:

values	a matrix for document vectors.
call	the command used to execute the function.

---

Latent Semantic Analysis model

Description

Train a Latent Semantic Analysis model (Deerwester et al., 1990) on a quanteda::tokens object.

Usage

textmodel_lsa(
  x,
  dim = 50,
  min_count = 5L,
  engine = c("RSpectra", "irlba", "rsvd"),
  weight = "count",
  verbose = FALSE,
  ...
)

Arguments

x	a quanteda::tokens object.
dim	the size of the word vectors.
min_count	the minimum frequency of the words. Words less frequent than this in x are removed before training.
engine	select the engine perform SVD to generate word vectors.
weight	weighting scheme passed to quanteda::dfm_weight().
verbose	if TRUE, print the progress of training.
...	additional arguments.

Value

Returns a textmodel_wordvector object with the following elements:

values	a matrix for word vectors values.
weights	a matrix for word vectors weights.
frequency	the frequency of words in x.
engine	the SVD engine used.
weight	weighting scheme.
concatenator	the concatenator in x.
call	the command used to execute the function.
version	the version of the wordvector package.

References

Deerwester, S. C., Dumais, S. T., Landauer, T. K., Furnas, G. W., & Harshman, R. A. (1990). Indexing by latent semantic analysis. JASIS, 41(6), 391–407.

Examples

library(quanteda)
library(wordvector)

# pre-processing
corp <- corpus_reshape(data_corpus_news2014)
toks <- tokens(corp, remove_punct = TRUE, remove_symbols = TRUE) %>% 
   tokens_remove(stopwords("en", "marimo"), padding = TRUE) %>% 
   tokens_select("^[a-zA-Z-]+$", valuetype = "regex", case_insensitive = FALSE,
                 padding = TRUE) %>% 
   tokens_tolower()

# train LSA
lsa <- textmodel_lsa(toks, dim = 50, min_count = 5, verbose = TRUE)

# find similar words
head(similarity(lsa, c("berlin", "germany", "france"), mode = "words"))
head(similarity(lsa, c("berlin" = 1, "germany" = -1, "france" = 1), mode = "values"))
head(similarity(lsa, analogy(~ berlin - germany + france)))

---

Word2vec model

Description

Train a Word2vec model (Mikolov et al., 2023) in different architectures on a quanteda::tokens object.

Usage

textmodel_word2vec(
  x,
  dim = 50,
  type = c("cbow", "skip-gram"),
  min_count = 5L,
  window = ifelse(type == "cbow", 5L, 10L),
  iter = 10L,
  alpha = 0.05,
  use_ns = TRUE,
  ns_size = 5L,
  sample = 0.001,
  normalize = TRUE,
  verbose = FALSE,
  ...
)

Arguments

x	a quanteda::tokens object.
dim	the size of the word vectors.
type	the architecture of the model; either "cbow" (continuous back of words) or "skip-gram".
min_count	the minimum frequency of the words. Words less frequent than this in x are removed before training.
window	the size of the word window. Words within this window are considered to be the context of a target word.
iter	the number of iterations in model training.
alpha	the initial learning rate.
use_ns	if TRUE, negative sampling is used. Otherwise, hierarchical softmax is used.
ns_size	the size of negative samples. Only used when use_ns = TRUE.
sample	the rate of sampling of words based on their frequency. Sampling is disabled when sample = 1.0
normalize	if TRUE, normalize the vectors in values and weights.
verbose	if TRUE, print the progress of training.
...	additional arguments.

Details

User can changed the number of processors used for the parallel computing via options(wordvector_threads).

Value

Returns a textmodel_wordvector object with the following elements:

values	a matrix for word vector values.
weights	a matrix for word vector weights.
dim	the size of the word vectors.
type	the architecture of the model.
frequency	the frequency of words in x.
window	the size of the word window.
iter	the number of iterations in model training.
alpha	the initial learning rate.
use_ns	the use of negative sampling.
ns_size	the size of negative samples.
concatenator	the concatenator in x.
call	the command used to execute the function.
version	the version of the wordvector package.

References

Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed Representations of Words and Phrases and their Compositionality. https://arxiv.org/abs/1310.4546.

Examples

library(quanteda)
library(wordvector)

# pre-processing
corp <- data_corpus_news2014 
toks <- tokens(corp, remove_punct = TRUE, remove_symbols = TRUE) %>% 
   tokens_remove(stopwords("en", "marimo"), padding = TRUE) %>% 
   tokens_select("^[a-zA-Z-]+$", valuetype = "regex", case_insensitive = FALSE,
                 padding = TRUE) %>% 
   tokens_tolower()

# train word2vec
w2v <- textmodel_word2vec(toks, dim = 50, type = "cbow", min_count = 5, sample = 0.001)

# find similar words
head(similarity(w2v, c("berlin", "germany", "france"), mode = "words"))
head(similarity(w2v, c("berlin" = 1, "germany" = -1, "france" = 1), mode = "values"))
head(similarity(w2v, analogy(~ berlin - germany + france), mode = "words"))

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[experimental] Extract word vector weights

Description

[experimental] Extract word vector weights

Usage

weights(x, mode = c("words", "values"))

Arguments

x	a textmodel_wordvector object.
mode	specify the type of resulting object.

Value

a matrix of word vector weights when mode = "value" or of words sorted in descending order by the weights when mode = "word".

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