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
The thesis focuses on learning syntactic tree structures by generalizing over an- notated treebanks....
We describe results of a novel algorithm for grammar induction from a large corpus. The ADIOS (Autom...
The paper describes an experiment in inside-outside estimation of a lexicalized probabilistic contex...
This thesis considers the problem of assigning a sentence its syntactic structure, which may be disc...
Parsing is the process of assigning structure to sentences. The structure is obtained from the gramm...
The development of frameworks that allow to state grammars for natural languages in a mathematically...
The development of frameworks that allow to state grammars for natural languages in a mathematically...
This paper examines the usefulness of corpus-derived probabilistic grammars as a basis for the auto-...
We show that under certain conditions, a language model can be trained oil the basis of a second lan...
We show that under certain conditions, a language model can be trained oil the basis of a second lan...
This paper presents a theory of the syntactic aspects of human sentence production. An important cha...
This paper examines the usefulness of corpus-derived probabilistic grammars as a basis for the autom...
This thesis investigates the problem of unsupervised learning of natural language grammar in the con...
Inducing a grammar from text has proven to be a notoriously challenging learning task despite decade...
Several applications of Natural Language Processing need short proces-sing times, while retaining a ...
The thesis focuses on learning syntactic tree structures by generalizing over an- notated treebanks....
We describe results of a novel algorithm for grammar induction from a large corpus. The ADIOS (Autom...
The paper describes an experiment in inside-outside estimation of a lexicalized probabilistic contex...
This thesis considers the problem of assigning a sentence its syntactic structure, which may be disc...
Parsing is the process of assigning structure to sentences. The structure is obtained from the gramm...
The development of frameworks that allow to state grammars for natural languages in a mathematically...
The development of frameworks that allow to state grammars for natural languages in a mathematically...
This paper examines the usefulness of corpus-derived probabilistic grammars as a basis for the auto-...
We show that under certain conditions, a language model can be trained oil the basis of a second lan...
We show that under certain conditions, a language model can be trained oil the basis of a second lan...
This paper presents a theory of the syntactic aspects of human sentence production. An important cha...
This paper examines the usefulness of corpus-derived probabilistic grammars as a basis for the autom...
This thesis investigates the problem of unsupervised learning of natural language grammar in the con...
Inducing a grammar from text has proven to be a notoriously challenging learning task despite decade...
Several applications of Natural Language Processing need short proces-sing times, while retaining a ...
The thesis focuses on learning syntactic tree structures by generalizing over an- notated treebanks....
We describe results of a novel algorithm for grammar induction from a large corpus. The ADIOS (Autom...
The paper describes an experiment in inside-outside estimation of a lexicalized probabilistic contex...