It is written in C/C++. One nice feature is that it supports the LETOR feature file format for ranking. They describe some of the algorithms in Large Scale Learning to Rank from NIPS where they presented algorithms for fast learners for approximate SVMs. The Combined Regression and Ranking (CRR) work was presented as a paper at KDD 2010.
The suite of fast incremental algorithms for machine learning (sofia-ml) can be used for training models for classification, regression, ranking, or combined regression and ranking. Several different techniques are available. This release is intended to aid researchers and practitioners who require fast methods for classification and ranking on large, sparse data sets.
The other package I recently discovered was Maui-Indexer. Maui-Indexer is an extension of the KEA key phrase extractor.
... it allows the assignment of topics to documents based on terms from Wikipedia using Wikipedia Miner. Maui also has many new features that help identify topics more accurately.
You can read more about it on the web page and read the publications.