Phrase Structure in a Computational Model O F Child Language Acquisition

This thesis describes a computational model of child language acquisition which acquires a recursive phrase-structure grammar in the absence of X-Bar Theory. The model assumes no grammar, lexicon, or segmentation. Input utterances include phrases as well as sentences, of no more than two levels of embedding, paired with their semantic representations. The initial products of acquisition are a lexicon of unanalysed utterances and a finite-state grammar. The lexical items acquired guide further lexical acquisition, which results in their segmentation, and thus triggers the acquisition of a phrase-structure grammar. The Lexical-Functional Grammar formalism is used, so that acquiring C-Structure, or phrase structure, can be viewed as mapping the ordered utterance onto the unordered F-Structure, a shallow semantic representation. Generalization over the phrase-structure rules acquired results in the induction of syntactic categories, and it is this which gives rise to recursion in the grammar. The model demonstrates both Degree-2 learnability and incremental learning in accordance with the gradual nature of child language development.