Procedure for quantitatively comparing the syntactic coverage of English grammars
HLT '91 Proceedings of the workshop on Speech and Natural Language
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Learning to Parse Natural Language with Maximum Entropy Models
Machine Learning - Special issue on natural language learning
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Logistic Regression, AdaBoost and Bregman Distances
Machine Learning
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Grafting: fast, incremental feature selection by gradient descent in function space
The Journal of Machine Learning Research
Statistical decision-tree models for parsing
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Feature selection, L1 vs. L2 regularization, and rotational invariance
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
Intricacies of Collins' Parsing Model
Computational Linguistics
Head-Driven Statistical Models for Natural Language Parsing
Computational Linguistics
Discriminative training of a neural network statistical parser
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Parsing the WSJ using CCG and log-linear models
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Incremental parsing with the perceptron algorithm
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Boosting-based parse reranking with subtree features
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Computational challenges in parsing by classification
CHSLP '06 Proceedings of the Workshop on Computationally Hard Problems and Joint Inference in Speech and Language Processing
Parsing with soft and hard constraints on dependency length
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
A classifier-based parser with linear run-time complexity
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Constituent parsing by classification
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Search-based structured prediction
Machine Learning
Perceptron training for a wide-coverage lexicalized-grammar parser
DeepLP '07 Proceedings of the Workshop on Deep Linguistic Processing
Quadratic features and deep architectures for chunking
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Computational challenges in parsing by classification
CHSLP '06 Proceedings of the Workshop on Computationally Hard Problems and Joint Inference in Speech and Language Processing
Training parsers by inverse reinforcement learning
Machine Learning
Incremental Sigmoid Belief Networks for Grammar Learning
The Journal of Machine Learning Research
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The present work advances the accuracy and training speed of discriminative parsing. Our discriminative parsing method has no generative component, yet surpasses a generative baseline on constituent parsing, and does so with minimal linguistic cleverness. Our model can incorporate arbitrary features of the input and parse state, and performs feature selection incrementally over an exponential feature space during training. We demonstrate the flexibility of our approach by testing it with several parsing strategies and various feature sets. Our implementation is freely available at: http://nlp.cs.nyu.edu/parser/.