SVM_HMM code and 10 document training set in the folder webpage_data
and a python script process_document.py that converts the labeled html
files into appopriate input for svmhmm.

Compile svmhmm by
make

Then convert the input in webpage_data like:
python process_document.py webpage_data > training.dat

(I suppose process_document.py should really be process_documents.py)

Train the model with:
./svm_hmm_learn -c 5000 -e 0.5 training.dat segmentation.model

Then, use the new model to classify that same training data with:
./svm_hmm_classify training.dat segmentation.model test.outtags

Congrats! You get about 93%. And that's probably with overfitting!

The actual output of the classification is in test.outtags which
can be (tediously or programmatically) compared to training.dat.