• Corpus ID: 18211380

Easy and Hard Constraint Ranking in OT: Algorithms and Complexity

@article{Eisner2000EasyAH,
  title={Easy and Hard Constraint Ranking in OT: Algorithms and Complexity},
  author={Jason Eisner},
  journal={ArXiv},
  year={2000},
  volume={cs.CL/0102019}
}
We consider the problem of ranking a set of OT constraints in a manner consistent with data. We speed up Tesar and Smolensky's RCD algorithm to be linear on the number of constraints. This finds a ranking so each attested form x_i beats or ties a particular competitor y_i. We also generalize RCD so each x_i beats or ties all possible competitors. Alas, this more realistic version of learning has no polynomial algorithm unless P=NP! Indeed, not even generation does. So one cannot improve… 

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