Introduction to algorithms
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
The theory of parsing, translation, and compiling
The theory of parsing, translation, and compiling
Text classification using string kernels
The Journal of Machine Learning Research
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 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
Learning to sportscast: a test of grounded language acquisition
Proceedings of the 25th international conference on Machine learning
Robotic vocabulary building using extension inference and implicit contrast
Artificial Intelligence
Learning to connect language and perception
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
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
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
Generative alignment and semantic parsing for learning from ambiguous supervision
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Learning language from its perceptual context
PADL'11 Proceedings of the 13th international conference on Practical aspects of declarative languages
Learning dependency-based compositional semantics
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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
Representing and resolving ambiguities in ontology-based question answering
TIWTE '11 Proceedings of the TextInfer 2011 Workshop on Textual Entailment
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 Bayesian approach to unsupervised semantic role induction
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Crosslingual induction of semantic roles
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Unsupervised PCFG induction for grounded language learning with highly ambiguous supervision
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Learning dependency-based compositional semantics
Computational Linguistics
Introduction to the special issue on learning semantics
Machine Learning
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This paper presents a method for learning a semantic parser from ambiguous supervision. Training data consists of natural language sentences annotated with multiple potential meaning representations, only one of which is correct. Such ambiguous supervision models the type of supervision that can be more naturally available to language-learning systems. Given such weak supervision, our approach produces a semantic parser that maps sentences into meaning representations. An existing semantic parsing learning system that can only learn from unambiguous supervision is augmented to handle ambiguous supervision. Experimental results show that the resulting system is able to cope up with ambiguities and learn accurate semantic parsers.