Stanford's Probabilistic Graphical Models class on Coursera will run again this August • /r/MachineLearning

CRF, HMM, MEMM - that I can do for sequence tagging, never tried it for something like this, it's probably extra hard. Although, I find probabilistic graphical models lacking and have redirected my efforts towards Learning to Search methods. If you check Structured models for fine-to-coarse sentiment analysis by McDonald et al. (2007), you'll see their structured prediction model is a CRF that is a bit hierarchical. You can use Leon Bottou's sgdcrf and adapt the model to get their model (little changes in the forward-backward and viterbi). The complexity of learning and inference for a single example for sentiment of document, paragraphs and sentences is O(M · (M2 P PM2 T)) O(M3 P T), where M is number of possible categorical values, P is number of paragraphs and T the average number of sentences in the paragraph. That's slow as fuck, although still fast if sgdcrf is used (about 500-2000 sentences per second).