Machine Learning
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
Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
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
Using decision trees to construct a practical parser
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Joint knowledge capture for grammars and ontologies
Proceedings of the 1st international conference on Knowledge capture
An integrated, dual learner for grammars and ontologies
Data & Knowledge Engineering
Shallow Parsing Using Probabilistic Grammatical Inference
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Shallow parsing using noisy and non-stationary training material
The Journal of Machine Learning Research
Sample Selection for Statistical Parsing
Computational Linguistics
Combining outputs of multiple Japanese named entity chunkers by stacking
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
NeurAlign: combining word alignments using neural networks
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Semantic parsing with structured SVM ensemble classification models
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Determining the Dependency Among Clauses Based on Machine Learning Techniques
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
GRAEL: an agent-based evolutionary computing approach for natural language grammar development
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A two-stage method for active learning of statistical grammars
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Learning an ensemble of semantic parsers for building dialog-based natural language interfaces
KI'06 Proceedings of the 29th annual German conference on Artificial intelligence
Evolutionary computing as a tool for grammar development
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
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
Improved fully unsupervised parsing with zoomed learning
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Comparison of various machine learning-based classifications of relative clauses
ACS'06 Proceedings of the 6th WSEAS international conference on Applied computer science
Bagging and Boosting statistical machine translation systems
Artificial Intelligence
A comparative study of classifier combination applied to NLP tasks
Information Fusion
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Bagging and boosting, two effective machine learning techniques, are applied to natural language parsing. Experiments using these techniques with a trainable statistical parser are described. The best resulting system provides roughly as large of a gain in F-measure as doubling the corpus size. Error analysis of the result of the boosting technique reveals some inconsistent annotations in the Penn Treebank, suggesting a semi-automatic method for finding inconsistent treebank annotations.