In this thesis, I present three supervised and one semi-supervised machine learning approach for improving statistical natural language dependency parsing. I first introduce a generative approach that uses a strictly lexicalised parsing model where all the parameters are based on words, without using any part-of-speech (POS) tags or grammatical categories. Then I present an improved large margi...

We describe our CoNLL 2008 Shared Task system in this paper. The system includes two cascaded components: a syntactic and a semantic dependency parsers. A firstorder projective MSTParser is used as our syntactic dependency parser. In order to overcome the shortcoming of the MSTParser, that it cannot model more global information, we add a relabeling stage after the parsing to distinguish some c...

In this paper, we propose a right-to-left dependency grammar parsing method for languages in which a governor appears after its modiier like Korean and Japanese. Unlike conventional left-to-right parsers, this parsing method can take advantage of the governor post-positioning property of such languages to reduce the size of search space by using the idea of a headable path. A headable path is a...

This paper explores the problem of parsing Chinese long sentences. Inspired by human sentence processing, a second-stage parsing method, referred as main structure parsing in this paper, are proposed to improve the parsing performance as well as maintaining its high accuracy and efficiency on Chinese long sentences. Three different methods have attempted in this paper and the result shows that ...

This paper presents an online algorithm for dependency parsing problems. We propose an adaptation of the passive and aggressive online learning algorithm to the dependency parsing domain. We evaluate the proposed algorithms on the 2007 CONLL Shared Task, and report errors analysis. Experimental results show that the system score is better than the average score among the participating systems.

Recently, there has been renewed interest in semantic dependency parsing, among which one of the paradigms focuses on parsing directed acyclic graphs (DAGs). Consideration of the decoding problem in natural language semantic dependency parsing as finding a maximum spanning DAG of a weighted directed graph carries many complexities. In particular, the computational complexity (and approximabilit...

Each year the Conference on Computational Natural Language Learning (CoNLL)1 features a shared task, in which participants train and test their systems on exactly the same data sets, in order to better compare systems. The tenth CoNLL (CoNLL-X) saw a shared task on Multilingual Dependency Parsing. In this paper, we describe how treebanks for 13 languages were converted into the same dependency ...

We present the Potsdam systems that participated in the semantic dependency parsing shared task of SemEval 2014. They are based on linguistically motivated bidirectional transformations between graphs and trees and on utilization of syntactic dependency parsing. They were entered in both the closed track and the open track of the challenge, recording a peak average labeled F1 score of 78.60.

We describe a generative model for nonprojective dependency parsing based on a simplified version of a transition system that has recently appeared in the literature. We then develop a dynamic programming parsing algorithm for our model, and derive an insideoutside algorithm that can be used for unsupervised learning of non-projective dependency trees.