An Efficient Algorithm for Projective Dependency Parsing

By Joakim Nivre. International Workshop/Conference on Parsing Technologies, 2003

499 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ab41136c0a73b42463db10f9dd2913f8d8735155

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

MaltParser: A Data-Driven Parser-Generator for Dependency Parsing (2006, background): “Deterministic parsing algorithms for building dependency graphs (Yamada and Matsumoto, 2003; Nivre, 2003)”
Training and Testing Low-degree Polynomial Data Mappings via Linear SVM (2010, methodology): “We study a transition-based parsing method proposed by Nivre (2003).”
A Gold Standard Dependency Corpus for English (2014, unknown): “We trained MaltParser (Nivre, 2003), a system for dependency parsing which can be used to induce a model from data, on two datasets.”
Incrementality in Deterministic Dependency Parsing (2004, methodology): “For a more detailed discussion of dependency graphs and well-formedness conditions, the reader is referred to Nivre (2003).”
Top-Down Nearly-Context-Sensitive Parsing (2010, result): “Since Nivre’s parser is a dependency parser this exact case does not apply (as it does not use CFG rules), but similar situations arise.”
Algorithms for Deterministic Incremental Dependency Parsing (2008, unknown): “We use the notation β c and A c to refer to the value of β and A , respectively, in a conﬁguration c ; we also use | β | to refer to the length of β (i.e., the number of nodes in the buffer) and we use [ ] to denote an empty buffer.”
Practical Semantic Parsing for Spoken Language Understanding (2019, background): “For a detailed explanation of transition-based parsing, see Nivre (2003) and Nivre (2008).”
brat: a Web-based Tool for NLP-Assisted Text Annotation (2012, methodology): “as chunking (Abney, 1991), Semantic Role Labeling (Gildea and Jurafsky, 2002; Carreras and Màrquez, 2005), and dependency annotation (Nivre, 2003) (See Figure 1 for examples).”
Transition-Based Dependency Parsing with Stack Long Short-Term Memory (2015, background): “Transition-based dependency parsing formalizes the parsing problem as a series of decisions that read words sequentially from a buffer and combine them incrementally into syntactic structures (Yamada and Matsumoto, 2003; Nivre, 2003; Nivre, 2004).”
Top Accuracy and Fast Dependency Parsing is not a Contradiction (2010, unknown): “The two main approaches to dependency parsing are transition based dependency parsing (Nivre, 2003; Yamada and Matsumoto., 2003; Titov and Henderson, 2007) and maximum spanning tree based dependency parsing (Eisner, 1996; Eisner, 2000; McDonald and Pereira, 2006).”
A Survey of the Usages of Deep Learning for Natural Language Processing (2020, background): “In the arc-standard approach [91], [92], all dependents are connected to a word before the word is connected to its parent.”
An Improved Non-monotonic Transition System for Dependency Parsing (2015, methodology): “This paper integrates innovations from Honnibal et al. (2013) and Nivre and Fernandez-Gonzalez (2014) to produce a novel non-monotonic set of transitions for transition-based dependency parsing.”