A corpus-based approach to language learning
A corpus-based approach to language learning
Natural language parsing as statistical pattern recognition
Natural language parsing as statistical pattern recognition
Using an annotated corpus as a stochastic grammar
EACL '93 Proceedings of the sixth conference on European chapter of the Association for Computational Linguistics
Efficiency, robustness and accuracy in Picky chart parsing
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
Inside-outside reestimation from partially bracketed corpora
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
Learning and Inference for Clause Identification
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Computational Linguistics
Robust German noun chunking with a probabilistic context-free grammar
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Robust parsing with weighted constraints
Natural Language Engineering
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Conditional structure versus conditional estimation in NLP models
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Parsing the WSJ using CCG and log-linear models
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Probabilistic CFG with latent annotations
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Learning accurate, compact, and interpretable tree annotation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Reformatting web documents via header trees
ACLdemo '05 Proceedings of the ACL 2005 on Interactive poster and demonstration sessions
Minimum risk annealing for training log-linear models
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Computational Linguistics
A psychologically plausible and computationally effective approach to learning syntax
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Loss minimization in parse reranking
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Dependency parsing by belief propagation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Lattice Minimum Bayes-Risk decoding for statistical machine translation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Shared logistic normal distributions for soft parameter tying in unsupervised grammar induction
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Parsing German with latent variable grammars
PaGe '08 Proceedings of the Workshop on Parsing German
Asynchronous binarization for synchronous grammars
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Fast consensus decoding over translation forests
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Products of random latent variable grammars
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Unsupervised syntactic alignment with inversion transduction grammars
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Variational inference for adaptor grammars
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Simple, accurate parsing with an all-fragments grammar
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Discriminative modeling of extraction sets for machine translation
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
A discriminative model for joint morphological disambiguation and dependency parsing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
The surprising variance in shortest-derivation parsing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Training a log-linear parser with loss functions via softmax-margin
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Spectral learning for non-deterministic dependency parsing
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Spectral learning of latent-variable PCFGs
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Improving NLP through marginalization of hidden syntactic structure
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Training factored PCFGs with expectation propagation
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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Many different metrics exist for evaluating parsing results, including Viterbi, Crossing Brackets Rate, Zero Crossing Brackets Rate, and several others. However, most parsing algorithms, including the Viterbi algorithm, attempt to optimize the same metric, namely the probability of getting the correct labelled tree. By choosing a parsing algorithm appropriate for the evaluation metric, better performance can be achieved. We present two new algorithms: the "Labelled Recall Algorithm," which maximizes the expected Labelled Recall Rate, and the "Bracketed Recall Algorithm," which maximizes the Bracketed Recall Rate. Experimental results are given, showing that the two new algorithms have improved performance over the Viterbi algorithm on many criteria, especially the ones that they optimize.