Recent advances of grammatical inference
Theoretical Computer Science - Special issue on algorithmic learning theory
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
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
A robot that uses existing vocabulary to infer non-visual word meanings from observation
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Acquiring word-meaning mappings for natural language interfaces
Journal of Artificial Intelligence Research
A bibliographical study of grammatical inference
Pattern Recognition
Cross-situational learning: a mathematical approach
EELC'06 Proceedings of the Third international conference on Emergence and Evolution of Linguistic Communication: symbol Grounding and Beyond
Bio-inspired grammatical inference
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
Towards a bio-computational model of natural language learning
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
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We present a simple computational model that includes semantics for language learning, as motivated by readings in the literature of children's language acquisition and by a desire to incorporate a robust notion of semantics in the field of Grammatical Inference. We argue that not only is it more natural to take into account semantics, but also that semantic information can make learning easier, and can give us a better understanding of the relation between positive data and corrections. We propose a model of meaning and denotation using finite-state transducers, motivated by an example domain of geometric shapes and their properties and relations. We give an algorithm to learn a meaning function and prove that it finitely converges to a correct result under a specific set of assumptions about the transducer and examples.