title: Adding sentence types to a model of syntactic category acquisition.
abstract: The acquisition of syntactic categories is a crucial step in the process of acquiring syntax. At this stage, before a full grammar is available, only surface cues are available to the learner. Previous computational models have demonstrated that local contexts are informative for syntactic categorization. However, local contexts are affected by sentence-level structure. In this paper, we add sentence type as an observed feature to a model of syntactic category acquisition, based on experimental evidence showing that pre-syntactic children are able to distinguish sentence type using prosody and other cues. The model, a Bayesian Hidden Markov Model, allows for adding sentence type in a few different ways; we find that sentence type can aid syntactic category acquisition if it is used to characterize the differences in word order between sentence types. In these models, knowledge of sentence type permits similar gains to those found by extending the local context.
authors.affiliation: Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh, UK. sfrank@inf.ed.ac.uk
authors.forename: Stella
authors.initials: S
authors.lastname: Frank
authors.forename: Sharon
authors.initials: S
authors.lastname: Goldwater
authors.forename: Frank
authors.initials: F
authors.lastname: Keller
mesh_terms: D001499:Bayes Theorem; D002675:Child, Preschool; D032882:Comprehension; D003198:Computer Simulation; D003463:Cues; D006801:Humans; D007223:Infant; D007802:Language; D007804:Language Development; D007858:Learning; D008390:Markov Chains
keywords: Bayesian modeling; Computational modeling; Language acquisition; Sentence prosody; Syntactic categories