Human language is an incredibly rich yet incredibly constrained system. Learning and generalizing these systematic constraints from small, sparse, and underspecified data presents a fundamental inference problem. Therapidity and ease by which humans learn these constraints has made this a foundational study in cognitive science, linguistics, and…

The past thirty years have shown a rise in models of language acquisition in which the state of the learner is characterized as a probability distribution over a set of non-stochastic grammars. In recent years, increasingly powerful models have been constructed as earlier models have failed to generalize well to increasingly complex and realistic…

Using a system based on standard augmented transition network (ATN) parsing approach, this report describes a technique for the rapid development of natural language parsing, called High-Level Grammar Specification Language (HGSL). The first part of the report describes the syntax and semantics of HGSL and the network implementation of each of its…