Dependency parsers and nite-state parsers are both capable of rapid and robust parsing of natural language. Dependency parsers produce richer output structures, while nite-state parsers can be more eecient. We show how a nite-state parser can be used to produce dependency structures for most phrase types, with an O(n 2) complexity in the number of words. The parser allows syntactically ambiguou...

Deterministic transition-based Shift/Reduce dependency parsers make often mistakes in the analysis of long span dependencies (McDonald & Nivre, 2007). Titov and Henderson (2007) address this accuracy drop by using a beam search instead of a greedy algorithm for predicting the next parser transition. We propose a parsing method that allows reducing several of these errors, although maintaining a...

We propose two general and robust methods for enriching resources annotated in the Frame Semantic paradigm with syntactic dependency graphs, which can provide useful additional information for applications such as semantic role labeling methods. One method incorporates information of a dependency parser, while the other one assumes the resource to be based on a treebank and uses dependency grap...

We present a novel method for the crosslingual transfer of dependency parsers. Our goal is to induce a dependency parser in a target language of interest without any direct supervision: instead we assume access to parallel translations between the target and one or more source languages, and to supervised parsers in the source language(s). Our key contributions are to show the utility of dense ...

We present the Dependency Parser, called Maxuxta, for the linguistic processing of Basque, which can serve as a representative of agglutinative languages that are also characterized by the free order of its constituents. The Dependency syntactic model is applied to establish the dependency-based grammatical relations between the components within the clause. Such a deep analysis is used to impr...

One of the main motivations for building treebanks is that they facilitate the development of syntactic parsers, by providing realistic data for evaluation as well as inductive learning. In this paper we present what we believe to be the first data-driven dependency parser for Urdu, which has developed using MaltParser system and trained and evaluated on data from Urdu dependency treebank. A 40...

This paper reports the corpus-oriented development of a wide-coverage Japanese HPSG parser. We first created an HPSG treebank from the EDR corpus by using heuristic conversion rules, and then extracted lexical entries from the treebank. The grammar developed using this method attained wide coverage that could hardly be obtained by conventional manual development. We also trained a statistical p...

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...

For many NLP applications that require a parser, the sentences of interest may not be well-formed. If the parser can overlook problems such as grammar mistakes and produce a parse tree that closely resembles the correct analysis for the intended sentence, we say that the parser is robust. This paper compares the performances of eight state-of-the-art dependency parsers on two domains of ungramm...