A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
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
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
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
Statistical decision-tree models for parsing
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Three new probabilistic models for dependency parsing: an exploration
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
A probability model to improve word alignment
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Intricacies of Collins' Parsing Model
Computational Linguistics
Phrasal cohesion and statistical machine translation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Online large-margin training of dependency parsers
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Machine translation using probabilistic synchronous dependency insertion grammars
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Bootstrapping path-based pronoun resolution
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Deterministic dependency parsing of English text
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Multilingual dependency parsing using Bayes Point Machines
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Improved large margin dependency parsing via local constraints and laplacian regularization
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Strictly lexical dependency parsing
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Using Short Dependency Relations from Auto-Parsed Data for Chinese Dependency Parsing
ACM Transactions on Asian Language Information Processing (TALIP)
Cross language dependency parsing using a bilingual lexicon
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 1 - Volume 1
Improving dependency parsing with subtrees from auto-parsed data
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Exploiting web-derived selectional preference to improve statistical dependency parsing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
EXPLOITING SUBTREES IN AUTO-PARSED DATA TO IMPROVE DEPENDENCY PARSING
Computational Intelligence
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Recently, significant progress has been made on learning structured predictors via coordinated training algorithms such as conditional random fields and maximum margin Markov networks. Unfortunately, these techniques are based on specialized training algorithms, are complex to implement, and expensive to run. We present a much simpler approach to training structured predictors by applying a boosting-like procedure to standard supervised training methods. The idea is to learn a local predictor using standard methods, such as logistic regression or support vector machines, but then achieve improved structured classification by "boosting" the influence of misclassified components after structured prediction, re-training the local predictor, and repeating. Further improvement in structured prediction accuracy can be achieved by incorporating "dynamic" features--i.e. an extension whereby the features for one predicted component can depend on the predictions already made for some other components. We apply our techniques to the problem of learning dependency parsers from annotated natural language corpora. By using logistic regression as an efficient base classifier (for predicting dependency links between word pairs), we are able to efficiently train a dependency parsing model, via structured boosting, that achieves state of the art results in English, and surpasses state of the art in Chinese.