Artificial grammar learning (AGL) is one of the most commonly used paradigms for the study of implicit learning and the contrast between rules, similarity, and associative learning. Despite five decades of extensive research, however, a satisfactory theoretical consensus has not been forthcoming

This paper proposes a formulation of grammar learning in which meaning plays a fundamental role. We present a computational model that aims to satisfy convergent constraints from cognitive lin-guistics and crosslinguistic developmental evidence within a statistically driven framework. The target

We present a computational model of grammar learning that combines domain-general learning mechanisms with rich representations of linguistic knowledge, world knowledge and situational and discourse context. These representations support processes of language understanding and inference (including

This paper proposes a formulation of grammar learning in which meaning plays a fundamental role. We present a computational model that aims to satisfy convergent constraints from cognitive linguistics and crosslinguistic developmental evidence within a statistically driven framework. The target

This paper describes Grammar Learning by Partition Search, a general method for automatically constructing grammars for a range of parsing tasks. Given a base grammar, a training corpus, and a parsing task, Partition Search constructs an optimised probabilistic context-free grammar by searching a

This paper describes Grammar Learning by Partition Search, a general method for automatically constructing grammars for a range of parsing tasks. Given a base grammar, a training corpus, and a parsing task, Partition Search constructs an optimised probabilistic context-free grammar by searching a

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This paper presents the ongoing project Computational Models of First Language Acquisition, together with its current product, the learning algorithm GraSp.

an approach to grammar extraction based on the state-space dynamics of the network. We conclude that statistical frequency-based and rule-based acquisition procedures can be viewed as complementary perspectives on grammar learning, and more generally, that classical cognitive models can be viewed as a special

This paper describes the Omphalos Context-Free Grammar Learning Competition held as part of the International Colloquium on Grammatical Inference 2004. The competition was created in an e#ort to promote the development of new and better grammatical inference algorithms for context