Procedure for quantitatively comparing the syntactic coverage of English grammars
HLT '91 Proceedings of the workshop on Speech and Natural Language
Statistical parsing of messages
HLT '90 Proceedings of the workshop on Speech and Natural Language
Learning to Parse Natural Language with Maximum Entropy Models
Machine Learning - Special issue on natural language learning
Parsing inside-out
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Generalized probabilistic LR parsing of natural language (Corpora) with unification-based grammars
Computational Linguistics - Special issue on using large corpora: I
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
New figures of merit for best-first probabilistic chart parsing
Computational Linguistics
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Using an annotated corpus as a stochastic grammar
EACL '93 Proceedings of the sixth conference on European chapter of the Association for Computational Linguistics
Pearl: a probabilistic chart parser
EACL '91 Proceedings of the fifth conference on European chapter of the Association for Computational Linguistics
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 DOP model for semantic interpretation
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
An empirical evaluation of LFG-DOP
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Computational complexity of probabilistic disambiguation by means of tree-grammars
COLING '96 Proceedings of the 16th conference on Computational linguistics - 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
Natural Language Engineering
An empirical evaluation of LFG-DOP
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
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
TüSBL: a similarity-based chunk parser for robust syntactic processing
HLT '01 Proceedings of the first international conference on Human language technology research
What is the minimal set of fragments that achieves maximal parse accuracy?
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
From chunks to function-argument structure: a similarity-based approach
ACL '01 Proceedings of the 39th 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
Getting Rid of Derivational Redundancy or How to Solve Kuhn's Problem
Minds and Machines
A unified model of structural organization in language and music
Journal of Artificial Intelligence Research
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
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
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Common wisdom has it that the bias of stochastic grammars in favor of shorter derivations of a sentence is harmful and should be redressed. We show that the common wisdom is wrong for stochastic grammars that use elementary trees instead of context-free rules, such as Stochastic Tree-Substitution Grammars used by Data-Oriented Parsing models. For such grammars a non-probabilistic metric based on the shortest derivation outperforms a probabilistic metric on the ATIS and OVIS corpora, while it obtains competitive results on the Wall Street Journal (WSJ) corpus. This paper also contains the first published experiments with DOP on the WSJ.