PsychWordVec
1.0.0
Han-Wu-Shuang (Bruce) Bao 包寒吳霜
? psychbruce.github.io
library( PsychWordVec ) for the APA-7 format of your installed version. # # Method 1: Install from CRAN
install.packages( " PsychWordVec " )
# # Method 2: Install from GitHub
install.packages( " devtools " )
devtools :: install_github( " psychbruce/ PsychWordVec " , force = TRUE )PsychWordVec embed | wordvec | |
|---|---|---|
| Basic class | matrix | data.table |
| Row size | vocabulary size | vocabulary size |
| Column size | dimension size | 2 (variables: word , vec ) |
| Advantage | faster (with matrix operation) | easier to inspect and manage |
| Function to get | as_embed() | as_wordvec() |
| Function to load | load_embed() | load_wordvec() |
: Note: Word embedding refers to a natural language processing technique that embeds word semantics into a low-dimensional embedding matrix , with each word (actually token) quantified as a numergronced Users 片形 v. suggested to import word vectors data as the embed class using the function load_embed() , which would automatically normalize all word vectors to the unit length 1 (see the normalize() PsychWordVec ) and
PsychWordVecas_embed() : from wordvec (data.table) to embed (matrix)as_wordvec() : from embed (matrix) to wordvec (data.table)load_embed() : load word embeddings data as embed (matrix)load_wordvec() : load word embeddings data as wordvec (data.table)data_transform() : transform plain text word vectors to wordvec or embedsubset() : extract a subset of wordvec and embednormalize() : normalize all word vectors to the unit length 1get_wordvec() : extract word vectorssum_wordvec() : calculate the sum vector of multiple wordsplot_wordvec() : visualize word vectorsplot_wordvec_tSNE() : 2D or 3D visualization with t-SNEorth_procrustes() : Orthogonal Procrustes matrix alignmentcosine_similarity() : cos_sim() or cos_dist()pair_similarity() : compute a similarity matrix of word pairsplot_similarity() : visualize similarities of word pairstab_similarity() : tabulate similarities of word pairsmost_similar() : find the Top-N most similar wordsplot_network() : visualize a (partial correlation) network graph of wordstest_WEAT() : WEAT and SC-WEAT with permutation test of significancetest_RND() : RND with permutation test of significancedict_expand() : expand a dictionary from the most similar wordsdict_reliability() : reliability analysis and PCA of a dictionarytokenize() : tokenize raw texttrain_wordvec() : train static word embeddingstext_init() : set up a Python environment for PLMtext_model_download() : download PLMs from Hugging Face to local ".cache" foldertext_model_remove() : remove PLMs from local ".cache" foldertext_to_vec() : extract contextualized token and text embeddingstext_unmask() : <deprecated> <please use FMAT> fill in the blank mask(s) in a querySee the documentation (help pages) for their usage and details.
