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Awesome GitHub RepositoriesLatent Semantic Models
Algorithms for identifying hidden thematic structures in text through unsupervised word co-occurrence analysis.
Distinct from Semantic Data Models: Distinct from Semantic Data Models: focuses on unsupervised thematic discovery in text rather than database schema abstraction.
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- piskvorky/gensim
piskvorky/gensim
16,361Auf GitHub ansehenGensim is a natural language processing toolkit designed for large-scale text analysis and the training of semantic vector embeddings. It provides a framework for identifying latent thematic structures within document collections and calculating semantic similarity between text segments using unsupervised statistical algorithms. The project is distinguished by its ability to handle datasets that exceed available system memory through incremental corpus streaming, which processes documents one at a time from disk. It utilizes sparse vector representations and dictionary-based token mapping to
Identifies latent thematic structures within document collections using unsupervised statistical algorithms.
Pythondata-miningdata-sciencedocument-similarity
