Induction, Training, and Parsing Strategies beyond Context-free Grammars
type: CreativeWork
description: This thesis considers the problem of assigning a sentence its syntactic structure, which may be discontinuous. It proposes a class of models based on probabilistic grammars that are obtained by the automatic refinement of a given grammar. Different strategies for parsing with a refined grammar are developed. The induction, refinement, and application of two types of grammars (linear context-free rewriting systems and hybrid grammars) are evaluated empirically on two German and one Dutch corpus.
abstract: This thesis considers the problem of assigning a sentence its syntactic structure, which may be discontinuous. It proposes a class of models based on probabilistic grammars that are obtained by the automatic refinement of a given grammar. Different strategies for parsing with a refined grammar are developed. The induction, refinement, and application of two types of grammars (linear context-free rewriting systems and hybrid grammars) are evaluated empirically on two German and one Dutch corpus.
author: Gebhardt, Kilian
keywords: discontinuous parsing, latent variable grammar, interpreted regular tree grammar, hybrid grammar, linear context-free rewriting system, syntactic parsing, Hybridgrammatiken, Diskontinuierliches Parsing, Syntaktisches Parsing
date published: 2020-05-06
date modified: 2026-07-24T03:56:21Z
provider: QUCOSA
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Technische Universität Dresden