The GloveR package is an R wrapper for the Global Vectors for Word Representation (GloVe). GloVe is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space. For more information consult : Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global Vectors for Word Representation. COPYRIGHTS file and LICENSE can be found in the inst folder of the R package.
This R package has some limitations:
- it works only on a unix OS
- the data file should be big enough for the package-function Glove to work properly
To install the package from Github use the install_github function of the devtools package,
devtools::install_github('mlampros/GloveR')
Use the following link to report bugs/issues (for the R wrapper),
https://github.com/mlampros/GloveR/issues
# example input data ---> 'dat.txt'
library(GloveR)
#-----------------------------
# vocabulary count computation
#-----------------------------
res = vocabulary_counts(train_data = '/data_GloveR/dat.txt', MAX_vocab = 0,
MIN_count = 5, output_vocabulary = '/data_GloveR/VOCAB.txt',
trace = TRUE)
#-------------------------
# cooccurrence statistics
#-------------------------
co_mat = cooccurrence_statistics(train_data = '/data_GloveR/dat.txt', vocab_input = '/data_GloveR/VOCAB.txt',
output_cooccurences = '/data_GloveR/COOCUR.bin', symmetric_both = TRUE,
context_words = 15, memory_gb = 4.0, MAX_product = 0, overflowLength = 0,
trace = TRUE)
#---------------------------
# shuffling of cooccurrences
#---------------------------
shfl = shuffle_cooccurrences(input_cooccurences = '/data_GloveR/COOCUR.bin',
output_cooccurences = '/data_GloveR/COOCUR_output.bin',
memory_gb = 4.0, arraySize = 0, trace = TRUE)
#---------------------------------------
# Global Vectors for Word Representation
#---------------------------------------
gl = Glove(input_cooccurences = '/data_GloveR/COOCUR_output.bin',
output_vectors = '/data_GloveR/vectors',
vocab_input = '/data_GloveR/VOCAB.txt',
model_output = 2, iter_num = 5, learn_rate = 0.05,
save_squared_grads_file = NULL, alpha_weight = 0.75,
cutoff = 10, binary_output = 0, vectorSize = 50, threads = 6,
trace = TRUE)
More information about the parameters of each function can be found in the package documentation.
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