Finite-state parsing and disambiguation
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 2
Active Feature-Value Acquisition for Classifier Induction
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
Using semantic relations to refine coreference decisions
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Regularized least-squares for parse ranking
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Regular approximation of link grammar
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
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We consider the problem of identifying among many candidates a single best solution which jointly maximizes several domain-specific target functions. Assuming that the candidate solutions can be generated incrementally, we model the error in prediction due to the incompleteness of partial solutions as a normally distributed random variable. Using this model, we derive a probabilistic search algorithm that aims at finding the best solution without the necessity to complete and rank all candidate solutions. We do not assume a Viterbi-type decoding, allowing a wider range of target functions. We evaluate the proposed algorithm on the problem of best parse identification, combining simple heuristic with more complex machine-learning based target functions. We show that the search algorithm is capable of identifying candidates with a very high score without completing a significant proportion of the candidate solutions.