text2vec is an R package which provides an efficient framework with a concise API for text analysis and natural language processing (NLP). Fast vectorization, topic modeling, distances and GloVe word embeddings in R.
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 RcppParallel package.
text2vec is inspired by gensim. The software is released under an open source license.
- Fast text vectorization (creation of document-term matrices) on arbitrary n-grams, using vocabulary or feature hashing.
- GloVe word embeddings.
- Topic modelling with:
- Latent Dirichlet Allocation – decomposes document-term matrix into two low-rank matrices – document-topic distribution and topic-word distribution. It’s a more complex non-linear generative model.
- Latent Semantic Analysis – oldest among topic modeling techniques. It also decomposes Document-Term matrix into a product of 2 low rank matrices.
- Provides 2 set of functions for measuring various distances/similarity in a unified way. All methods are written with special attention to computational performance and memory efficiency. Functions for fast calculation of similarity between documents (specifically similarities and distances between matrices):
- Cosine distance – classical approach from computational linguistics is to measure similarity based on the content overlap between documents.
- Jaccard distance – a simple but intuitive measure of similarity between two sets.
- Relaxed Word Mover’s Distance.
- Euclidean – with text2vec it can only be computed on dense matrices.
- Streaming API means that users do not have to load all the data into RAM:
- I/O handling.
- Easy parallel processing – provides convenient functions for easy parallel processing of text.
- Models – provides unified interface for models, which is inspired by scikit-learn interface. Models in text2vec are mostly transformers and decompositions.
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