Adaptor grammars (Johnson et al., 2007b) are a non-parametric Bayesian extension of Probabilistic Context-Free Grammars (PCFGs) which in effect learn the probabilities of entire s...
Adaptor grammars are a framework for expressing and performing inference over a variety of non-parametric linguistic models. These models currently provide state-of-the-art perfor...
This paper uses an unsupervised model of grounded language acquisition to study the role that social cues play in language acquisition. The input to the model consists of (orthogr...