Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning to Parse Natural Language with Maximum Entropy Models
Machine Learning - Special issue on natural language learning
Discriminative Reranking for Natural Language Parsing
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Parsing inside-out
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Coping with ambiguity and unknown words through probabilistic models
Computational Linguistics - Special issue on using large corpora: II
PCFG models of linguistic tree representations
Computational Linguistics
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Three generative, lexicalised models for statistical parsing
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
A probabilistic corpus-driven model for lexical-functional analysis
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Statistical decision-tree models for parsing
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
A new statistical parser based on bigram lexical dependencies
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Parsing with the shortest derivation
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Stochastic lexicalized tree-adjoining grammars
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Three new probabilistic models for dependency parsing: an exploration
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Tree-gram parsing lexical dependencies and structural relations
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
An improved parser for data-oriented lexical-functional analysis
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Statistical parsing with an automatically-extracted tree adjoining grammar
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Statistical parsing with a context-free grammar and word statistics
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
A General Parsing Model for Music and Language
ICMAI '02 Proceedings of the Second International Conference on Music and Artificial Intelligence
The Journal of Machine Learning Research
An efficient implementation of a new DOP model
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Neural network probability estimation for broad coverage parsing
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Immediate-head parsing for language models
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Inducing history representations for broad coverage statistical parsing
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Head-Driven Statistical Models for Natural Language Parsing
Computational Linguistics
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Antecedent recovery: experiments with a trace tagger
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Boosting-based parse reranking with subtree features
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Reducing Bias Effects in DOP Parameter Estimation
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
A unified model of structural organization in language and music
Journal of Artificial Intelligence Research
Bayesian learning of a tree substitution grammar
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Weight pushing and binarization for fixed-grammar parsing
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
Simple, accurate parsing with an all-fragments grammar
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Factors affecting the accuracy of Korean parsing
SPMRL '10 Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages
Judging grammaticality with tree substitution grammar derivations
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
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
Accurate parsing with compact tree-substitution grammars: Double-DOP
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Toward Tree Substitution Grammars with latent annotations
WILS '12 Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure
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We aim at finding the minimal set of fragments which achieves maximal parse accuracy in Data Oriented Parsing. Experiments with the Penn Wall Street Journal treebank show that counts of almost arbitrary fragments within parse trees are important, leading to improved parse accuracy over previous models tested on this treebank (a precision of 90.8% and a recall of 90.6%). We isolate some dependency relations which previous models neglect but which contribute to higher parse accuracy.