The Generation of Phrase-Structure Representation from Principles

Implementations of grammatical theory have traditionally been based upon Context-Free Grammar (CFG) formalisms which all but ignore questions of learnability. Even implementations which are based upon theories of Generative Grammar (GG), a paradigm which is supposedly motivated by learnability, rarely address such questions. In this thesis we will examine a GG theory which has been formulated primarily to address questions of learnability and present an implementation based upon this theory. The theory argues from Chomsky's definition of epistemological priority that principles which match elements and structures from prelinguistic systems with elements and structures in linguistic systems are preferable to those which are defined purely linguistically or non-linguistically. A procedure for constructing phrase-structure representations from prelinguistic relations using principles of node percolation (rather than the traditional $ \overline{X}$-theory of GG theories or phrase-structure rules of CFG theories) is presented and this procedure integrated into a left-right, primarily bottom-up parsing mechanism. Specifically, we present a parsing mechanism which derives phrase-structure representations of sentences from Case- and $\Theta $-relations using a small number of Percolation Principles. These Percolation Principles simply determine the categorial features of the dominant node of any two adjacent nodes in a representational tree, doing away with explicit phrase structure rules altogether. The parsing mechanism also instantiates appropriate empty categories using a filler-driven paradigm for leftward argument and non-argument movement. Procedures modelling learnability are not implemented in this work, but the applicability of the presented model to a computational model of language is discussed.