| Type: | Package |
| Title: | A Model for Semi-Supervised Keyword Extraction from Word Embedding Models |
| Version: | 1.2.5 |
| Description: | A fast and computationally efficient algorithm designed to enable researchers to efficiently and quickly extract semantically-related keywords using a fitted embedding model. For more details about the methods applied, see Chester (2025). <doi:10.17605/OSF.IO/5B7RQ>. |
| Encoding: | UTF-8 |
| License: | GPL-3 |
| Depends: | R (≥ 4.1.0) |
| Imports: | data.table (≥ 1.14.8), textstem (≥ 0.1.4) |
| Suggests: | knitr, R.utils, rmarkdown, spelling, testthat |
| LinkingTo: | Rcpp |
| LazyData: | TRUE |
| LazyDataCompression: | xz |
| RoxygenNote: | 7.3.2 |
| Language: | en-US |
| NeedsCompilation: | yes |
| Packaged: | 2025-05-30 01:35:12 UTC; patrick |
| Author: | Patrick Chester [aut, cre] |
| Maintainer: | Patrick Chester <patrickjchester@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-06-03 09:30:09 UTC |
Returns cosine cosimilarity matrix for the terms generated by keyclust
Description
A function that extracts the cosimilarity matrix for
terms generated by keyclust()
Usage
cosimilarity_matrix(x)
Arguments
x |
output from |
Value
An N x N matrix of cosine cosimilarity values, where n is the number of terms in the provided embedding model
Algorithm designed to efficiently extract keywords from a cosine similarity matrix
Description
This function takes a cosine similarity matrix derived from a word embedding model, along with a set of seed words and outputs a semantically-related set of keywords of a length and cosimilarity determined by the user
Usage
keyclust(
sim_mat,
seed_words,
sim_thresh = 0.25,
max_n = 50,
dictionary = NULL,
exclude = NULL,
verbose = TRUE
)
Arguments
sim_mat |
A cosine similarity matrix produced by cosine. |
seed_words |
A set of user-provided seed words that best represent the target concept. |
sim_thresh |
Minimum cosine similarity a candidate word must have to the existing set of keywords for it to be added. |
max_n |
The maximum size of the output set of keywords. |
dictionary |
An optional dictionary that maps metadata, such as definitions, to keywords. |
exclude |
A vector of words that the user does not want included in the final keyword set. |
verbose |
If true, keyclust will produce live updates as it adds keywords. |
Value
A list containing a data frame of keywords and their cosine similarities, and a matrix of cosine similarities.
Examples
# Create a set of keywords using a pre-defined set of seeds
seeds <- c("october", "november")
# Create a cosine similarity matrix from a word embedding model
simmat_FasttextEng_sample <- wordemb_FasttextEng_sample |>
process_embed(words='words') |>
similarity_matrix(words = "words")
# Use keyclust to generate a set of keywords
months <- keyclust(simmat_FasttextEng_sample, seed_words = seeds, max_n = 8)
Prints terms generated by keyclust
Description
Prints terms generated by keyclust
Usage
## S3 method for class 'keyclust'
print(x, ...)
Arguments
x |
output from |
... |
additional arguments not used |
Value
A message indicating the number of keywords produced and a preview of the first few keywords.
A tool designed to reduce redundant terms in a fitted embedding model
Description
Takes a fitted embedding model as an input. Allows users to combine embeddings by the case, stem, or lemma of associated terms.
Usage
process_embed(
x,
words = NULL,
punct = TRUE,
tolower = TRUE,
lemmatize = TRUE,
stem = FALSE
)
Arguments
x |
A fitted word embedding model in the data frame format |
words |
The name of a column that corresponds to the word dimension of the fitted word embeddings |
punct |
Removes punctuation |
tolower |
Combines terms that differ by case |
lemmatize |
Combines terms that share a common lemma. Uses the lexicon package by default. |
stem |
Combines terms that share a common stem. Note: Stemming should not be used in conjunction with lemmatize. |
Value
A data frame with the same columns as the input, but with redundant terms combined.
Algorithm designed to create a cosine similarity matrix from a fitted word embedding model
Description
This function takes a fitted word embedding model and computes the cosine similarity between each word.
Usage
similarity_matrix(x, words = NULL, max_terms = 25000)
Arguments
x |
A word embedding matrix |
words |
A vector of words or the name of a column that corresponds to the word dimension of the fitted word embeddings |
max_terms |
The maximum number of embedding terms that will be included in output similarity matrix. Assumes that embedding input is ordered by word frequency. |
Value
An N x N matrix of cosine similarity scores between words from a fitted word embedding model.
Examples
# Create a set of keywords using a pre-defined set of seeds
simmat <- similarity_matrix(wordemb_FasttextEng_sample, words = "words")
Returns terms generated by keyclust
Description
A function that returns the terms and their cosine cosimilarities
produced by keyclust()
Usage
## S3 method for class 'keyclust'
terms(x, ...)
Arguments
x |
output from |
... |
additional arguments not used |
Value
A data frame of terms and their cosine similarities.
Sample from the pre-trained English fastText model
Description
This is a data frame containing the 2,000 most frequently occurring terms from Facebook's English-language fastText word embeddings model.
Usage
wordemb_FasttextEng_sample
Format
A 2000 row and 301 column data frame. The row represents the word embedding term, while the numeric columns represent the word embedding dimension. The character column gives the terms associated with each word vector.
References
P. Bojanowski*, E. Grave*, A. Joulin, T. Mikolov, Enriching Word Vectors with Subword Information (arxiv)
Examples
data(wordemb_FasttextEng_sample)
head(wordemb_FasttextEng_sample)