From the webpage:
Semantic Vector indexes, created by applying a Random Projection algorithm to term-document matrices created using Apache Lucene. The package was created as part of a project by the University of Pittsburgh Office of Technology Management, and is now developed and maintained by contributors from the University of Texas, Queensland University of Technology, the Austrian Research Institute for Artificial Intelligence, Google Inc., and other institutions and individuals.
The package creates a WordSpace model, of the kind developed by Stanford University’s Infomap Project and other researchers during the 1990s and early 2000s. Such models are designed to represent words and documents in terms of underlying concepts, and as such can be used for many semantic (concept-aware) matching tasks such as automatic thesaurus generation, knowledge representation, and concept matching.
The Semantic Vectors package uses a Random Projection algorithm, a form of automatic semantic analysis. Other methods supported by the package include Latent Semantic Analysis (LSA) and Reflective Random Indexing. Unlike many other methods, Random Projection does not rely on the use of computationally intensive matrix decomposition algorithms like Singular Value Decomposition (SVD). This makes Random Projection a much more scalable technique in practice. Our application of Random Projection for Natural Language Processing (NLP) is descended from Pentti Kanerva’s work on Sparse Distributed Memory, which in semantic analysis and text mining, this method has also been called Random Indexing. A growing number of researchers have applied Random Projection to NLP tasks, demonstrating:
- Semantic performance comparable with other forms of Latent Semantic Analysis.
- Significant computational performance advantages in creating and maintaining models.
So, after reading about random indexing, etc., you can take those techniques out for a spin. It doesn’t get any better than that!