Fast and memory-friendly tools for text vectorization, topic modeling (LDA, LSA), word embeddings (GloVe), similarities. This package provides a source-agnostic streaming API, which allows researchers to perform analysis of collections of documents which are larger than available RAM. All core functions are parallelized to benefit from multicore machines.
You've just discovered text2vec!
text2vec is an R package which provides an efficient framework with a concise API for text analysis and natural language processing (NLP).
Goals which we aimed to achieve as a result of development of
The core functionality at the moment includes
Author of the package is a little bit obsessed about efficiency.
This package is efficient because it is carefully written in C++, which also means that text2vec is memory friendly. Some parts (such as GloVe) are fully parallelized using the excellent RcppParallel package. This means that the word embeddings are computed in parallel on OS X, Linux, Windows, and even Solaris (x86) without any additional tuning or tricks.
Other emrassingly parallel tasks (such as vectorization) can use any parallel backend which supports foreach package. They can achieve near-linear scalability with number of available cores.
Finally, a streaming API means that users do not have to load all the data into RAM.
The package has issue tracker on GitHub where I'm filing feature requests and notes for future work. Any ideas are appreciated.
Contributors are welcome. You can help by:
GPL (>= 2)
collocation_stat- were never used internally. Users can easily calculate ranks themselves
prune_vocabulary- filter by document counts
dist2performamce for RWMD - incorporate ideas from gensim PR discussion.
data.framewith meta-information in attributes (stopwords, ngram, number of docs, etc).
lda_cfrom formats in DTM construction
itoken_parallelhigh-level functions for parallel computing
chunks_numerparameter renamed to
create_corpusfrom public API, moved co-occurence related optons to
create_tcm. Now package relies on sparsepp library for underlying hash maps.
2016-10-03. See 0.4 milestone tags.
doc_proportions. see #52.
prune_vocabulary. signature also was changed.
transform_*- more intuitive + simpler usage with autocompletion
itoken. Simplifies assignement of ids to rows of DTM
create_vocabularynow can handle
First CRAN release of text2vec.