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
Following natural language route instructions
Following natural language route instructions
Learning to sportscast: a test of grounded language acquisition
Proceedings of the 25th international conference on Machine learning
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
Transforming meaning representation grammars to improve semantic parsing
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Walk the talk: connecting language, knowledge, and action in route instructions
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
A generative model for parsing natural language to meaning representations
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Acquiring word-meaning mappings for natural language interfaces
Journal of Artificial Intelligence Research
Optimizing Chinese word segmentation for machine translation performance
StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation
Learning to follow navigational route instructions
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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
Following directions using statistical machine translation
Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction
Toward understanding natural language directions
Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction
Learning to follow navigational directions
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
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
Generative alignment and semantic parsing for learning from ambiguous supervision
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Collecting highly parallel data for paraphrase evaluation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Learning dependency-based compositional semantics
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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
Reducing grounded learning tasks to grammatical inference
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Learning from natural instructions
Machine Learning
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Learning a semantic lexicon is often an important first step in building a system that learns to interpret the meaning of natural language. It is especially important in language grounding where the training data usually consist of language paired with an ambiguous perceptual context. Recent work by Chen and Mooney (2011) introduced a lexicon learning method that deals with ambiguous relational data by taking intersections of graphs. While the algorithm produced good lexicons for the task of learning to interpret navigation instructions, it only works in batch settings and does not scale well to large datasets. In this paper we introduce a new online algorithm that is an order of magnitude faster and surpasses the state-of-the-art results. We show that by changing the grammar of the formal meaning representation language and training on additional data collected from Amazon's Mechanical Turk we can further improve the results. We also include experimental results on a Chinese translation of the training data to demonstrate the generality of our approach.