Tutorial

Authors Clark, Alexander Year 2017 Abstract Learnability has traditionally been considered to be a crucial constraint on theoretical syntax; however, the issues involved have been poorly understood, partly as a result of the lack of simple learning algorithms for various types of formal grammars. Here I discuss the computational issues involved in learning hierarchically structured grammars from strings of symbols alone. The methods involved are based on an abstract notion of the derivational context of a syntactic category, which in the most elementary case of context-free grammars leads to learning algorithms based on a form of traditional distributional analysis. [Read More]

Authors Clark, Alexander and Yoshinaka, Ryo Year 2016 Abstract Natural languages require grammars beyond context-free for their description. Here we extend a family of distributional learning algorithms for context-free grammars to the class of Parallel Multiple Context-Free Grammars (PMCFGs). These grammars have two additional operations beyond the simple context-free operation of concatenation: the ability to interleave strings of symbols, and the ability to copy or duplicate strings. This allows the grammars to generate some non-semilinear languages, which are outside the class of mildly context-sensitive grammars. [Read More]

Authors

Clark, Alexander

Year

2014

Abstract

This is a gentle introduction to Multiple Context Free Grammars (mcfgs), intended for linguists who are familiar with context free grammars and movement based analyses of displaced constituents, but unfamiliar with Minimalist Grammars or other mildly context-sensitive formalisms.

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