Knowledge representation: logical, philosophical and computational foundations
Knowledge representation: logical, philosophical and computational foundations
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Wide-coverage semantic representations from a CCG parser
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Learning constraint-based grammars from representative examples: theory and applications
Learning constraint-based grammars from representative examples: theory and applications
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Question answering using ontological semantics
TextMean '04 Proceedings of the 2nd Workshop on Text Meaning and Interpretation
A statistical semantic parser that integrates syntax and semantics
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
A learnable constraint-based grammar formalism
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Ontology-Based semantic interpretation as grammar rule constraints
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
Learning for deep language understanding
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
| Hi-index | 0.00 |
We argue in favor of using a graph-based representation for language meaning and propose a novel learning method to map natural language text to its graph-based meaning representation. We present a grammar formalism, which combines syntax and semantics, and has ontology constraints at the rule level. These constraints establish links between language expressions and the entities they refer to in the real world. We present a relational learning algorithm that learns these grammars from a small representative set of annotated examples, and show how this grammar induction framework and the ontology-based semantic representation allow us to directly map text to graph-based meaning representations.