This paper is concerned with the problem of heterogeneous dependency parsing. In this paper, we present a novel joint inference scheme, which is able to leverage the consensus information between heterogeneous treebanks in the parsing phase. Different from stacked learning methods (Nivre and McDonald, 2008; Martins et al., 2008), which process the dependency parsing in a pipelined way (e.g., a ...

This paper describes a system for syntacticsemantic dependency parsing for multiple languages. The system consists of three parts: a state-of-the-art higher-order projective dependency parser for syntactic dependency parsing, a predicate classifier, and an argument classifier for semantic dependency parsing. For semantic dependency parsing, we explore use of global features. All components are ...

Dependency-based methods for syntactic parsing have become increasingly popular in natural language processing in recent years. This book gives a thorough introduction to the methods that are most widely used today. After an introduction to dependency grammar and dependency parsing, followed by a formal characterization of the dependency parsing problem, the book surveys the three major classes...

The paper describes our system of Shared Task on Parsing the Web. We only participate in dependency parsing task. A number of methods have been developed for dependency parsing. Each of the methods adopts very different view of dependency parsing, and each view can have its strengths and limitations. Thus system combination can have great potential to further improve the performance of dependen...

Abstract Dependency-based approaches to syntactic analysis assume that structure can be analyzed in terms of binary asymmetric dependency relations holding between elementary units. Computational models for parsing almost universally an unit is a word, while the influential theory Lucien Tesnière instead posits more abstract notion nucleus, which may realized as one or words. In this article, w...

We propose a novel natural language processing task, ReliAble dependency arc recognition (RADAR), which helps high-level applications better utilize the dependency parse trees. We model RADAR as a binary classification problem with imbalanced data, which classifies each dependency parsing arc as correct or incorrect. A logistic regression classifier with appropriate features is trained to recog...