Automated deduction by theory resolution
Journal of Automated Reasoning
Prolog and natural-language analysis
Prolog and natural-language analysis
Class-based n-gram models of natural language
Computational Linguistics
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Monotonic semantic interpretation
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
Three new probabilistic models for dependency parsing: an exploration
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
From trees to predicate-argument structures
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Online large-margin training of dependency parsers
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Wide-coverage semantic representations from a CCG parser
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Training and Testing Low-degree Polynomial Data Mappings via Linear SVM
The Journal of Machine Learning Research
Unsupervised dependency parsing without gold part-of-speech tags
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Learning dependency-based compositional semantics
Computational Linguistics
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We present a method for training a statistical model for mapping natural language sentences to semantic expressions. The semantics are expressions of an underspecified logical form that has properties making it particularly suitable for statistical mapping from text. An encoding of the semantic expressions into dependency trees with automatically generated labels allows application of existing methods for statistical dependency parsing to the mapping task (without the need for separate traditional dependency labels or parts of speech). The encoding also results in a natural per-word semantic-mapping accuracy measure. We report on the results of training and testing statistical models for mapping sentences of the Penn Treebank into the semantic expressions, for which per-word semantic mapping accuracy ranges between 79% and 86% depending on the experimental conditions. The particular choice of algorithms used also means that our trained mapping is deterministic (in the sense of deterministic parsing), paving the way for large-scale text-to-semantic mapping.