done with training instance gen for scoring model.
- read segmented sentences train.seg and build lattice train.lattice (calling lattice_gen.py)
- traverse train.lattice to visit each bigram state <bigram, incoming_char> and generate its features using feature_gen.py
- collect instane, label = "T" if the state is in the gold-standard segmentation, "F" otherwise
*trivial* clean ipynb