Papers for Relevance Assessment by Joakim Nivre

Research Question: Can non-projective dependencies be captured with an accuracy sufficient to outperform the best projective dependency parsers? [The answer turned out to be no in our study.]

Reformulation: Can non-projective dependencies be captured with an accuracy sufficient to outperform the best projective dependency parsers?

Paper ID
(Link to PDF)

Title

Author(s)

W05-1504

Parsing with Soft and Hard Constraints on Dependency Length

Jason Eisner; Noah A. Smith

10-248

Deterministic Dependency Parsing of English Text

Joakim Nivre and Mario Scholz

P03-1046

Parsing with Generative Models of Predicate-Argument Structure

Julia Hockenmaier

137_pdf_2-col

Enriching the Output of a Parser Using Memory-based Learning

Valentin Jijkoun and Maarten de Rijke

P01-1010

What is the Minimal Set of Fragments that Achieves Maximal Parse Accuracy?

Rens Bod

300_pdf_2-col

Alternative approaches for Generating Bodies of Grammar Rules

Gabriel Infante-Lopez and Maarten de Rijke

H05-1102

Incremental LTAG Parsing

Libin Shen; Aravind Joshi

341_pdf_2-col

Corpus-Based Induction of Syntactic Structure: Models of Dependency and Constituency

Dan Klein and Christopher Manning

H05-1066

Non-Projective Dependency Parsing using Spanning Tree Algorithms

Ryan McDonald; Fernando Pereira; Kiril Ribarov; Jan Hajic

W00-1303

Japanese Dependency Structure Analysis Based on Support Vector Machines

Taku Kudo; Yuji Matsumoto

P05-1012

Online Large-Margin Training of Dependency Parsers

Ryan McDonald; Koby Crammer; Fernando Pereira

W05-1516

Strictly Lexical Dependency Parsing

Qin Iris Wang; Dale Schuurmans; Dekang Lin

W05-1505

Corrective Modeling for Non-Projective Dependency Parsing

Keith Hall; Vaclav Novak

P03-2006

Finding Non-local Dependencies: Beyond Pattern Matching

Valentin Jijkoun

I05-2044

Two-Phase Shift-Reduce Deterministic Dependency Parser of Chinese

Meixun Jin; Mi-Young Kim; Jong-Hyeok Lee