Natural language understanding (2nd ed.)
Natural language understanding (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A Brief Introduction to Boosting
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
PCFG models of linguistic tree representations
Computational Linguistics
Bagging and boosting a treebank parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Integrating top-down and bottom-up approaches in inductive logic programming: applications in natural language processing and relational data mining
Learning to transform natural to formal languages
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
A statistical semantic parser that integrates syntax and semantics
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Learning to parse database queries using inductive logic programming
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Transforming meaning representation grammars to improve semantic parsing
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Subtree mining for question classification problem
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Driving semantic parsing from the world's response
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Confidence driven unsupervised semantic parsing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Bootstrapping semantic parsers from conversations
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Lexical generalization in CCG grammar induction for semantic parsing
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
A semi supervised learning model for mapping sentences to logical form with ambiguous supervision
NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems
A Two-Phase Framework for Learning Logical Structures of Paragraphs in Legal Articles
ACM Transactions on Asian Language Information Processing (TALIP)
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We present a learning framework for structured support vector models in which boosting and bagging methods are used to construct ensemble models. We also propose a selection method which is based on a switching model among a set of outputs of individual classifiers when dealing with natural language parsing problems. The switching model uses subtrees mined from the corpus and a boosting-based algorithm to select the most appropriate output. The application of the proposed framework on the domain of semantic parsing shows advantages in comparison with the original large margin methods.