An Information Retrieval Tutorial on Cosine Similarity Measures, Dot Products and Term Weight Calculations.

LETOR is a benchmark dataset for research on learning to rank, released by Microsoft Research Asia.

Java-based framework designed to support the development of applications for unsupervised machine learning tasks, with a particular focus on their application to text data

Semantic Vector indexes, created by applying a Random Projection algorithm to term-document matrices created using Apache Lucene. 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, similar to Latent Semantic Analysis (LSA) and its variants like Probabilistic Latent Semantic Analysis (PLSA).