Dependency parser is one of the most important fundamental tools in the natural language processing, which extracts structure of sentences and determines the relations between words based on the dependency grammar. The dependency parser is proper for free order languages, such as Persian. In this paper, data-driven dependency parser has been developed with the help of phrase-structure parser fo...

This paper describes the dependency parser we used in the NLP Tools Contest, 2009 for parsing Hindi, Bangla and Telugu. The parser uses a bidirectional parsing algorithm with two operations proj and non-proj to build the dependency tree. The parser obtained Labeled Attachment Score of 71.63%, 59.86% and 67.74% for Hindi, Telugu and Bangla respectively on the treebank with fine-grained dependenc...

This paper introduces a parser system for the meta grammar formalism of Extensible Dependency Grammar (XDG). XDG is a generalisation of Topological Dependency Grammar (TDG) (Duchier and Debusmann, 2001). The XDG parser system comprises a constraintbased parser for all possible instances of XDG, a statically typed grammar input language, and a flexible backend for handling parser output. A power...

This paper presents experiments which combine a grammar-driven and a datadriven parser. We show how the conversion of LFG output to dependency representation allows for a technique of parser stacking, whereby the output of the grammar-driven parser supplies features for a data-driven dependency parser. We evaluate on English and German and show significant improvements stemming from the propose...

We present a two-stage framework to parse a sentence into its Abstract Meaning Representation (AMR). We first use a dependency parser to generate a dependency tree for the sentence. In the second stage, we design a novel transition-based algorithm that transforms the dependency tree to an AMR graph. There are several advantages with this approach. First, the dependency parser can be trained on ...

We present a flexible open-source framework that performs dependency parsing with collapsed dependencies. The parser framework features a rule-based annotator that directly works on the output of a dependency parser. Thus, it can introduce dependency collapsing and propagation (de Marneffe et al., 2006) to parsers that lack this functionality. Collapsing is a technique for dependency parses whe...

Dependency parsers are critical components within many NLP systems. However, currently available dependency parsers each exhibit at least one of several weaknesses, including high running time, limited accuracy, vague dependency labels, and lack of nonprojectivity support. Furthermore, no commonly used parser provides additional shallow semantic interpretation, such as preposition sense disambi...

To solve the data sparseness problem in dependency parsing, most previous studies used features extracted from large-scale auto-parsed data. Unlike previous work, we propose a novel approach to improve dependency parsing with dependency triples (DT) extracted by self-disambiguating patterns (SDP). The use of SDP makes it possible to avoid the dependency on a baseline parser and explore the infl...