Efficient learning of context-free grammars from positive structural examples
Information and Computation
Inferring pure context-free languages from positive data
Acta Cybernetica
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
Learning k-Reversible Context-Free Grammars from Positive Structural Examples
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
PAC Learning with Simple Examples
STACS '96 Proceedings of the 13th Annual Symposium on Theoretical Aspects of Computer Science
Robotic vocabulary building using extension inference and implicit contrast
Artificial Intelligence
On the relationship between lexical semantics and syntax for the inference of context-free grammars
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
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
Inferring grammars for mildly context sensitive languages in polynomial-time
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
| Hi-index | 0.00 |
Computational approaches to language acquisition typically reduce the problem to one of either learning syntax, or learning a lexicon--isolated tasks. These problem simplifications have led to disconnected solutions and algorithms with no expectation of grounded inputs. We propose a language learning framework for bootstrapping the acquisition of a grammar and learning lexical semantics. We describe the framework and provide an instantiation using context-free grammars and a lambda-calculus semantic representation.