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,Scalable *2vec training word2vec and doc2vec algorithms,Deep learning with word2vec and Gensim as well as latent semantic analysis (LSA, LSI, SVD), non-negative matrix factorization (NMF), latent Dirichlet allocation (LDA), tf-idf and random projections.Radim Řehůřek and Petr Sojka (2010). Software framework for topic modelling with large corpora. Proc. LREC Workshop on New Challenges for NLP Frameworks
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.
Uses of Gensim
Gensim library has been used and cited in over 1400 commercial and academic applications as of 2018,Gensim academic citations in a diverse array of disciplines from medicine to insurance claim analysis to patent search.Commercial adopters of Gensim The software has been covered in several new articles, podcasts and interviews.Podcast.__init__ episode #71 on GensimInterview with Radim Řehůřek, creator of Gensim
Free and commercial support
The open source code is developed and hosted on GitHubGensim source code on Github and a public support forum is maintained on Google GroupsGensim mailing list on Google Groups and Gitter.Gensim chat room on Gitter
See also
Comparison of machine learning software Natural Language Toolkit SpaCy
References
External links