This paper presents two Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference of probabilistic context free grammars (PCFGs) from terminal strings, providing an altern...
Mark Johnson, Thomas L. Griffiths, Sharon Goldwate...
Customization to specific domains of discourse and/or user requirements is one of the greatest challenges for today’s Information Extraction (IE) systems. While demonstrably eff...
This paper explores the potential for annotating and enriching data for low-density languages via the alignment and projection of syntactic structure from parsed data for resource...
An open issue in data-driven dependency parsing is how to handle non-projective dependencies, which seem to be required by linguistically adequate representations, but which pose ...
This paper reports experiments in which pCRU — a generation framework that combines probabilistic generation methodology with a comprehensive model of the generation space — i...