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modify get_sparse_features.py to fire more information
for nouns:
changed sing->pl
changed pl->sing
for verbs:
changed from_pos->to_pos etc..
compute alpha(eps) and beta(eps)
using the deletions from training corpus...
Global number of changes factor
need to think about this... we want the graphical model to make as few edits as possible.
Filter agiga for data that is relevant for tune and test set
aspect for training raw text
We need aspect information on the training data.
We are going to use POS information to assign the aspect.
e.g. assume if we have the following POS file
DT NN VBD DT ...
We want the following aspect file
XX nsg past XX ... (XX is a filler.)
Precompute lm scores for candidate bigram
blocked by #1
Generate Candidate bigrams
Pronoun candidates
modify get_vform_candidates.py to print conjugation features
how to manage def make_gec_instances() for entire training set
see tagger-unsup-gec.py.
def make_gec_instances
get pos lm scores
@keisks do you want to assign this to yourself?
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