Using an HMM that has been initialized with grammar-based transition probabilities, combined with a two-stage integer programming strategy, this approach can achieve single-best accuracy of 64.3% on ambiguous supertags.
the identifier and classifier models are then trained from these features, instead of from features obtained from gold-standard syntactic derivations.