Gensim is a free Python framework designed to automatically extract semantic topics from documents, as efficiently (computer-wise) and painlessly (human-wise) as possible. Gensim aims at processing raw, unstructured digital texts (“plain text”). The algorithms in gensim, such as Latent Semantic Analysis, Latent Dirichlet Allocation or Random Projections, discover semantic structure of documents, by examining word statistical co-occurrence patterns within a corpus of training documents. These algorithms are unsupervised, which means no human input is necessary – you only need a corpus of plain text documents. Once these statistical patterns are found, any plain text documents can be succinctly expressed in the new, semantic representation, and queried for topical similarity against other documents.
Keywords for this software
References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Kaliszyk, Cezary; Urban, Josef: MizAR 40 for Mizar 40 (2015)
- Mu, Tingting; Miwa, Makoto; Tsujii, Junichi; Ananiadou, Sophia: Discovering robust embeddings in (dis)similarity space for high-dimensional linguistic features (2014)
- Borbinha, José; Bouche, Thierry; Nowiński, Aleksander; Sojka, Petr: Project EuDML -- a first year demonstration (2011)