Notes:
Gensim is an open-source toolkit for vector space modeling and topic modeling, implemented in the Python programming language. It is designed to be fast and efficient, and it is widely used in a variety of natural language processing (NLP) tasks, such as document classification, topic extraction, and semantic similarity analysis.
Vector space modeling is a mathematical technique used in NLP to represent the meaning of words and documents in a way that allows them to be compared and analyzed. Gensim includes algorithms for creating and manipulating vector space models, such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA).
Topic modeling is another NLP technique that is used to automatically discover the hidden themes or topics in a collection of documents. Gensim includes algorithms for performing topic modeling, such as LDA and Hierarchical Dirichlet Process (HDP).
Overall, Gensim is a powerful and versatile toolkit that is widely used in NLP research and applications. It is open-source, so it is freely available for anyone to use and modify.
Wikipedia:
See also:
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