Description:

Library for Vector Space Modelling with very large corpora. Target audience is the Natural Language Processing (NLP) community.

Features:

• all algorithms are memory-independent w.r.t. the corpus size
• low API learning curve, simple interfaces
• efficient implementations of popular algorithms, such as incremental online Latent Semantic Analysis, Latent Dirichlet Allocation or Random Projections
• can run Latent Semantic Analysis on a cluster of computers (distributed computing)

Changes to previous version:

• improved Latent Semantic Analysis (incremental SVD) performance: factorizing the English Wikipedia (3.1m documents) now takes 14h even in serial mode (i.e., on a single computer)
• several minor optimizations and bug fixes

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Platform Independent

Data Formats: Agnostic

Tags: Latent Semantic Analysis, Latent Dirichlet Allocation, Svd, Random Projections, Tfidf

Archive: download here

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