I planed to write something for Semi-supervised Learning.  However, after reading  Zhu Xiaojin's PhD dissertation (now Assistant Professor at UWisconsin), I find it far better to read his thesis.  He also maintains a web page that puts together the historical and cutting edge research in semi-supervised learning.  The work is up-to-date (May 2005), clear and comprehensive.  Proudly, I got my Bachelor's degree from the same university as Prof. Zhu, Shanghai Jiao Tong University (though he graduated and left the university 3 years before I was admitted) .

Here is a list (must be incomplete) of recent papers (up to early 2005) on machine learning with structured data.

There is a huge number of optimization tools/software available online.  What I like to use is TAO, based on PETSc.  This package utilized MPI, thus it is very suitable for maximum entropy models and CRF.  In fact, we used 30 processors to compute the objective function value and gradient (expectations) in parallel, by uniformly distributing data examples to all processors available and  then assemble their contribution to calculate the gradient.