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Gensim is a robust open-source vector space modeling and topic modeling toolkit implemented in Python. It uses NumPy, SciPy and optionally Cython for performance. Gensim is specifically designed to handle large text collections, using data streaming and efficient incremental algorithms, which differentiates it from most other scientific software packages that only target batch and in-memory processing.

Gensim
Gensim logo.png
Original author(s) Radim Řehůřek
Developer(s) RaRe Technologies, various
Initial release 2009
Stable release
2.3.0 / 25 July 2017; 50 days ago (2017-07-25)
Repository github.com/RaRe-Technologies/gensim
Development status active
Written in Python
Operating system Linux, Windows, macOS, OS X
Platform cross-platform
Type Information retrieval
License LGPL
Website radimrehurek.com/gensim/

Contents

Main featuresEdit

Gensim includes implementations of tf-idf, random projections, word2vec and document2vec algorithms,[1] hierarchical Dirichlet processes (HDP), latent semantic analysis (LSA) and latent Dirichlet allocation (LDA), including distributed parallel versions.[2]

Some of the online algorithms in Gensim were also published in the 2011 PhD dissertation Scalability of Semantic Analysis in Natural Language Processing of Radim Řehůřek, the creator of Gensim.[3]

Uses of gensimEdit

Gensim has been used and cited in over 500 commercial and academic applications.[4][5] The software has been covered in several new articles, podcasts and interviews since 2009.[6][7][8]

Free and commercial supportEdit

The open source code is developed and hosted on GitHub[9] and a public support forum is maintained on Google Groups[10] and Gitter.[11]

Gensim is commercially supported by the company rare-technologies.com, who also provide student mentorships and academic thesis projects for gensim via their Student Incubator programme.[12]

ReferencesEdit

External linksEdit