* Play with NER feats a bit. Up to 82.00 training on MUC7.
* Fix context vector, importing all features
* Add extra context tokens
* Refine word_shape feature, by trimming the max sequence length
* Increase default number of iterations from 5 to 10
* When encoding POS/NER tags, accept '-' as a missing value
* Upd shape test
* Upd NER feats. With adadelta learner, getting 76.9 on NER
* Upd fabfile for experiments
* Moving tagger to accept multiple correct answers
* Refactor context extraction, and start breaking out gold standards into their own functions
* Work on shift-reduce NER
* Add some tests for ner
* Bug fixes to NER
* Add tests for NER oracle
* Add curr field to State, to handle entity being built
* Implement validation and oracle on pystate, for testing
* Fix moves for new State struct
* Fixed greedy NER parsing. With static oracle, replicates accuracy from tagger.
* Use greedy NER parser