Gensim

Gensim
Original author(s)	Radim Řehůřek
Developer(s)	RARE Technologies Ltd.
Initial release	2009
Repository	github.com/RaRe-Technologies/gensim
Written in	Python
Operating system	Linux, Windows, macOS
Type	Information retrieval
License	LGPL
Website	radimrehurek.com/gensim/

Short description: Vector space modeling and topic modeling toolkit

Gensim is an open-source library for unsupervised topic modeling, document indexing, retrieval by similarity, and other natural language processing functionalities, using modern statistical machine learning.

Gensim is implemented in Python and Cython for performance. Gensim is designed to handle large text collections using data streaming and incremental online algorithms, which differentiates it from most other machine learning software packages that target only in-memory processing.

Main Features

Gensim includes streamed parallelized implementations of fastText,^[1] word2vec and doc2vec algorithms,^[2] as well as latent semantic analysis (LSA, LSI, SVD), non-negative matrix factorization (NMF), latent Dirichlet allocation (LDA), tf-idf and random projections.^[3]

Some of the novel 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.^[4]

Uses of Gensim

Gensim library has been used and cited in over 1400 commercial and academic applications as of 2018,^[5] in a diverse array of disciplines from medicine to insurance claim analysis to patent search.^[6] The software has been covered in several new articles, podcasts and interviews.^[7]^[8]^[9]

Free and Commercial Support

The open source code is developed and hosted on GitHub^[10] and a public support forum is maintained on Google Groups^[11] and Gitter.^[12]

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.^[13]

References

↑ Scalable *2vec training
↑ Deep learning with word2vec and Gensim
↑ Radim Řehůřek and Petr Sojka (2010). Software framework for topic modelling with large corpora. Proc. LREC Workshop on New Challenges for NLP Frameworks
↑ Řehůřek, Radim (2011). "Scalability of Semantic Analysis in Natural Language Processing". http://radimrehurek.com/phd_rehurek.pdf. Retrieved 27 January 2015. "my open-source gensim software package that accompanies this thesis"
↑ Gensim academic citations
↑ Commercial adopters of Gensim
↑ Podcast.__init__ episode #71 on Gensim
↑ Interview with Radim Řehůřek, creator of Gensim
↑ "DecisionStats Interview Radim Řehůřek Gensim #python". 8 December 2015. http://decisionstats.com/2015/12/07/decisionstats-interview-radim-rehurek-gensim-python/.
↑ Gensim source code on Github
↑ Gensim mailing list on Google Groups
↑ Gensim chat room on Gitter
↑ Gensim open source Incubator

External links

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Original source: https://en.wikipedia.org/wiki/Gensim. Read more

[1] Scalable *2vec training

[2] Deep learning with word2vec and Gensim

[3] Radim Řehůřek and Petr Sojka (2010). Software framework for topic modelling with large corpora. Proc. LREC Workshop on New Challenges for NLP Frameworks

[4] Řehůřek, Radim (2011). "Scalability of Semantic Analysis in Natural Language Processing". http://radimrehurek.com/phd_rehurek.pdf. Retrieved 27 January 2015. "my open-source gensim software package that accompanies this thesis"

[5] Gensim academic citations

[6] Commercial adopters of Gensim

[7] Podcast.__init__ episode #71 on Gensim

[8] Interview with Radim Řehůřek, creator of Gensim

[9] "DecisionStats Interview Radim Řehůřek Gensim #python". 8 December 2015. http://decisionstats.com/2015/12/07/decisionstats-interview-radim-rehurek-gensim-python/.

[10] Gensim source code on Github

[11] Gensim mailing list on Google Groups

[12] Gensim chat room on Gitter

[13] Gensim open source Incubator

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