Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
Computational optimality theory
Computational optimality theory
Dynamics of complex systems
Efficient generation in primitive Optimality Theory
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Lexical disambiguation using simulated annealing
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 1
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The input data to grammar learning algorithms often consist of overt forms that do not contain full structural descriptions. This lack of information may contribute to the failure of learning. Past work on Optimality Theory introduced Robust Interpretive Parsing (RIP) as a partial solution to this problem. We generalize RIP and suggest replacing the winner candidate with a weighted mean violation of the potential winner candidates. A Boltzmann distribution is introduced on the winner set, and the distribution's parameter $$T$$ is gradually decreased. Finally, we show that GRIP, the Generalized Robust Interpretive Parsing Algorithm significantly improves the learning success rate in a model with standard constraints for metrical stress assignment.