CoNLL 2006

39 papers

Dependency Parsing as a Classication Problem

Deniz Yuret

The Exploration of Deterministic and Efficient Dependency Parsing

Yu-Chieh Wu, Yue-Shi Lee, Jie-Chi Yang

Maximum Spanning Tree Algorithm for Non-projective Labeled Dependency Parsing

Nobuyuki Shimizu

Language Independent Probabilistic Context-Free Parsing Bolstered by Machine Learning

Michael Schiehlen, Kristina Spranger

Multi-lingual Dependency Parsing with Incremental Integer Linear Programming

Sebastian Riedel, Ruket Çakıcı, Ivan Meza-Ruiz

Labeled Pseudo-Projective Dependency Parsing with Support Vector Machines

Joakim Nivre, Johan Hall, Jens Nilsson, Gülşen Eryiǧit, Svetoslav Marinov

Multilingual Dependency Analysis with a Two-Stage Discriminative Parser

Ryan Mcdonald, Kevin Lerman, Fernando Pereira

Dependency Parsing Based on Dynamic Local Optimization

Ting Liu, Jinshan Ma, Huijia Zhu, Sheng Li

Investigating Multilingual Dependency Parsing

Richard Johansson, Pierre Nugues

Vine Parsing and Minimum Risk Reranking for Speed and Precision

Markus Dreyer, David A. Smith, Noah A. Smith

Dependency Parsing with Reference to Slovene, Spanish and Swedish

Simon Corston-Oliver, Anthony Aue

Multi-lingual Dependency Parsing at NAIST

Yuchang Cheng, Masayuki Asahara, Yuji Matsumoto

A Pipeline Model for Bottom-Up Dependency Parsing

Ming-Wei Chang, Quang Do, Dan Roth

Projective Dependency Parsing with Perceptron

Xavier Carreras, Mihai Surdeanu, Lluis Marquez

Dependency Parsing by Inference over High-recall Dependency Predictions

Sander Canisius, Toine Bogers, Antal van den Bosch, Jeroen Geertzen, Erik Tjong Kim Sang

LingPars, a Linguistically Inspired, Language-Independent Machine Learner for Dependency Treebanks

Eckhard Bick

Experiments with a Multilanguage Non-Projective Dependency Parser

Giuseppe Attardi

The Treebanks Used in the Shared Task

Yuval Krymolowski

CoNLL-X Shared Task on Multilingual Dependency Parsing

Sabine Buchholz, Erwin Marsi

A Context Pattern Induction Method for Named Entity Extraction

Partha Pratim Talukdar, Thorsten Brants, Mark Liberman, Fernando Pereira

Using Gazetteers in Discriminative Information Extraction

Andrew Smith, Miles Osborne

Learning Auxiliary Fronting with Grammatical Inference

Alexander Clark, Rémi Eyraud

Unsupervised Grammar Induction by Distribution and Attachment

David J. Brooks

Which Side are You on? Identifying Perspectives at the Document and Sentence Levels

Wei-Hao Lin, Theresa Wilson, Janyce Wiebe, Alexander Hauptmann

Word Distributions for Thematic Segmentation in a Support Vector Machine Approach

Maria Georgescul, Alexander Clark, Susan Armstrong

A Lattice-Based Framework for Enhancing Statistical Parsers with Information from Unlabeled Corpora

Michaela Atterer, Hinrich Schütze

Unsupervised Parsing with U-DOP

Rens Bod

Applying Alternating Structure Optimization to Word Sense Disambiguation

Rie Kubota Ando

Can Human Verb Associations Help Identify Salient Features for Semantic Verb Classification?

Sabine Schulte im Walde

Semantic Role Labeling via Tree Kernel Joint Inference

Alessandro Moschitti, Daniele Pighin, Roberto Basili

Semantic Role Recognition Using Kernels on Weighted Marked Ordered Labeled Trees

Jun’ichi Kazama, Kentaro Torisawa

Investigating Lexical Substitution Scoring for Subtitle Generation

Oren Glickman, Ido Dagan, Walter Daelemans, Mikaela Keller, Samy Bengio

Resolving and Generating Definite Anaphora by Modeling Hypernymy using Unlabeled Corpora

Nikesh Garera, David Yarowsky

What are the Productive Units of Natural Language Grammar? A DOP Approach to the Automatic Identification of Constructions.

Willem Zuidema

Improved Large Margin Dependency Parsing via Local Constraints and Laplacian Regularization

Qin Iris Wang, Colin Cherry, Dan Lizotte, Dale Schuurmans

Non-Local Modeling with a Mixture of PCFGs

Slav Petrov, Leon Barrett, Dan Klein

Porting Statistical Parsers with Data-Defined Kernels

Ivan Titov, James Henderson

A Mission for Computational Natural Language Learning

Walter Daelemans

Proceedings of the Tenth Conference on Computational Natural Language Learning (CoNLL-X)

Lluis Marquez, Dan Klein