Dependency Grammar Induction
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Benchmarks
Most implemented
Second-Order Unsupervised Neural Dependency Parsing
CRF Autoencoder for Unsupervised Dependency Parsing
Unsupervised Neural Dependency Parsing
Papers
Second-Order Unsupervised Neural Dependency Parsing
Most of the unsupervised dependency parsers are based on first-order probabilistic generative models that only consider local parent-child information. Inspired by second-order supervised dependency parsing, we proposed …
Dependency Grammar InductionDependency ParsingEnhancing Unsupervised Generative Dependency Parser with Contextual Information
Most of the unsupervised dependency parsers are based on probabilistic generative models that learn the joint distribution of the given sentence and its parse. Probabilistic generative models usually explicit decompose t…
Constituency Grammar InductionDependency Grammar InductionSentenceUnsupervised Dependency ParsingDependency Grammar Induction with a Neural Variational Transition-based Parser
Dependency grammar induction is the task of learning dependency syntax without annotated training data. Traditional graph-based models with global inference achieve state-of-the-art results on this task but they require …
Dependency Grammar InductionVariational InferenceCRF Autoencoder for Unsupervised Dependency Parsing
Unsupervised dependency parsing, which tries to discover linguistic dependency structures from unannotated data, is a very challenging task. Almost all previous work on this task focuses on learning generative models. In…
Dependency Grammar InductionUnsupervised Dependency ParsingCombining Generative and Discriminative Approaches to Unsupervised Dependency Parsing via Dual Decomposition
Unsupervised dependency parsing aims to learn a dependency parser from unannotated sentences. Existing work focuses on either learning generative models using the expectation-maximization algorithm and its variants, or l…
ClusteringDependency Grammar InductionUnsupervised Dependency ParsingDependency Grammar Induction with Neural Lexicalization and Big Training Data
We study the impact of big models (in terms of the degree of lexicalization) and big data (in terms of the training corpus size) on dependency grammar induction. We experimented with L-DMV, a lexicalized version of Depen…
Dependency Grammar Induction