An Efficient Algorithm for Easy-First Non-Directional Dependency Parsing
Explore this paper's citation graph
Summary
A novel deterministic dependency parsing algorithm that attempts to create the easiest arcs in the dependency structure first in a non-directional manner, which is significantly more accurate than best-first transition based parsers, and nears the performance of globally optimized parsing models.
- Type
- article
- Published
- 2010-06-02
- Cited by
- 219
- References
- 24
- OpenAlex
- https://openalex.org/W1660460062
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:3146611
Keywords
Parsing, Computer science, Dependency grammar, Traverse, Dependency (UML)
References
- An English dependency treebank à la Tesnière
- Experiments with a Higher-Order Projective Dependency Parser
- Multilingual Dependency Parsing Using Global Features
- Statistical Dependency Analysis with Support Vector Machines
- Online Learning of Approximate Dependency Parsing Algorithms
- Characterizing the Errors of Data-Driven Dependency Parsing Models
- Fast and Robust Multilingual Dependency Parsing with a Generative Latent Variable Model
- MaltParser: A Data-Driven Parser-Generator for Dependency Parsing
- A Tale of Two Parsers: Investigating and Combining Graph-based and Transition-based Dependency Parsing
- Parsing with Soft and Hard Constraints on Dependency Length
- Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms
- CoNLL-X Shared Task on Multilingual Dependency Parsing
- Incrementality in Deterministic Dependency Parsing
- Vine Parsing and Minimum Risk Reranking for Speed and Precision
- Integrating Graph-Based and Transition-Based Dependency Parsers
- Online Large-Margin Training of Dependency Parsers
- Incremental Integer Linear Programming for Non-projective Dependency Parsing
- Parser Combination by Reparsing
- LTAG Dependency Parsing with Bidirectional Incremental Construction
- Bilingually-Constrained (Monolingual) Shift-Reduce Parsing
Cited by
- Identifying Broken Plurals, Irregular Gender, and Rationality in Arabic Text
- Detecting Dependency Parse Errors with Minimal Resources
- Third-order Variational Reranking on Packed-Shared Dependency Forests
- Domain Adaptation of a Dependency Parser with a Class-Class Selectional Preference Model
- Learning-based Multi-Sieve Co-reference Resolution with Knowledge
- Syntax-Based Grammaticality Improvement using CCG and Guided Search
- Learning Greedy Policies for the Easy-First Framework
- On generating coherent multilingual descriptions of museum objects from Semantic Web ontologies
- Syntactic Dependency Parsers for Biomedical-NLP
- Easy-first Coreference Resolution
- Integrating learning and search for structured prediction
- Efficient large-context dependency parsing and correction with distributional lexical resources. (Analyse syntaxique probabiliste en de'pendances : approches efficaces à large contexte avec ressources lexicales distributionnelles)
- A Dynamic Oracle for Arc-Eager Dependency Parsing
- Efficient Non-deterministic Search in Structured Prediction: A Case Study on Syntactic Parsing
- A Joint Model for Extended Semantic Role Labeling
- Dependency Parsing Techniques for Information Extraction
- Analyzing the CoNLL-X Shared Task from a Sentence Accuracy Perspective
- An Empirical Comparison of Parsing Methods for Stanford Dependencies
- A Fast, Accurate, Non-Projective, Semantically-Enriched Parser
- Learning with Lookahead: Can History-Based Models Rival Globally Optimized Models?
Related papers
- Transition-based Dependency Parsing with Rich Non-local Features
- Building a Large Annotated Corpus of English: The Penn Treebank
- An Efficient Algorithm for Projective Dependency Parsing
- Statistical Dependency Analysis with Support Vector Machines
- Dynamic Programming for Linear-Time Incremental Parsing
- Online Large-Margin Training of Dependency Parsers
- Algorithms for Deterministic Incremental Dependency Parsing
- CoNLL-X Shared Task on Multilingual Dependency Parsing
- Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms
- Non-Projective Dependency Parsing in Expected Linear Time