On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Class-based n-gram models of natural language
Computational Linguistics
Natural language understanding (2nd ed.)
Natural language understanding (2nd ed.)
A maximum entropy approach to natural language processing
Computational Linguistics
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
Using string-kernels for learning semantic parsers
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Learning for semantic parsing with statistical machine translation
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Semantic parsing with structured SVM ensemble classification models
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Semi-supervised learning for semantic parsing using support vector machines
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Learning to transform natural to formal languages
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Learning language semantics from ambiguous supervision
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Learning semantic correspondences with less supervision
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
A statistical semantic parser that integrates syntax and semantics
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Bootstrapping semantic analyzers from non-contradictory texts
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Training a multilingual sportscaster: using perceptual context to learn language
Journal of Artificial Intelligence Research
Learning to parse database queries using inductive logic programming
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Driving semantic parsing from the world's response
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Inducing probabilistic CCG grammars from logical form with higher-order unification
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
A Bayesian model for 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
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Semantic parsing is the task of mapping a natural sentence to a meaning representation. The limitation of semantic parsing is that it is very difficult to obtain annotated training data in which a sentence is paired with a semantic representation. To deal with this problem, we introduce a semi supervised learning model for semantic parsing with ambiguous supervision. The main idea of our method is to utilize a large amount of data, to enrich feature space with the maximum entropy model using our semantic learner. We evaluate the proposed models on standard corpora to show that our methods are suitable for semantic parsing problem. Experimental results show that the proposed methods work efficiently and well on ambiguous data and it is comparable to the state of the art method.