The S-Space Package is a Natural Language Processing library for
distributional semantics representations. Distributional semantics
representations model the meaning of words, phrases, and sentences as high
dimensional vectors or probability distributions. The library includes common
algorithms such as Latent Semantic Analysis, Random Indexing, and Latent
Dirichlet Allocation. The S-Space package also includes software libraries
for matrices, vectors, graphs, and numerous clustering
algorithms.
The S-Space Package is a Natural Language Processing library for
distributional semantics representations. Distributional semantics
representations model the meaning of words, phrases, and sentences as high
dimensional vectors or probability distributions. The library includes common
algorithms such as Latent Semantic Analysis, Random Indexing, and Latent
Dirichlet Allocation. The S-Space package also includes software libraries
for matrices, vectors, graphs, and numerous clustering
algorithms.
The S-Space Package is a collection of algorithms for building Semantic Spaces as well as a highly-scalable library for designing new distributional semantics algorithms. Distributional algorithms process text corpora and represent the semantic for words as high dimensional feature vectors. These approaches are known by many names, such as word spaces, semantic spaces, or distributed semantics and rest upon the Distributional Hypothesis: words that appear in similar contexts have similar meanings.
The research and development is being done by the Natural Language Processing group at UCLA led by David Jurgens and Keith Stevens, under the advisory of Dr. Michael Dyer.
See the Getting Started page for a quick introduction on how to use the S-Space package, see the Package Overview for information on the code and available features, or dive right into the Javadoc to see what's available now. For any questions, please contact us via our mailing lists: S-Space-Users and S-Space-Research-Dev.
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