Transition-based dependency parsing as latent-variable constituent parsing

We provide a theoretical argument that a common form of projective transition-based dependency parsing is less powerful than constituent parsing using latent variables. The argument is a proof that, under reasonable assumptions, a transition-based dependency parser can be converted to a latent-variable context-free grammar producing equivalent structures.Postprin

Calculating the optimal step in shift-reduce dependency parsing : from cubic to linear time

We present a new cubic-time algorithm to calculate the optimal next step in shift-reduce dependency parsing, relative to ground truth, commonly referred to as dynamic oracle. Unlike existing algorithms, it is applicable if the training corpus contains non-projective structures. We then show that for a projective training corpus, the time complexity can be improved from cubic to linear.Publisher PDFPeer reviewe

Discovering latent structures in syntax trees and mixed-type data

Gibbs sampling is a widely applied algorithm to estimate parameters in statistical models. This thesis uses Gibbs sampling to resolve practical problems, especially on natural language processing and mixed type data. It includes three independent studies. The first study includes a Bayesian model for learning latent annotations. The technique is capable of parsing sentences in a wide variety of languages, producing results that are on-par with or surpass previous approaches in accuracy, and shows promising potential for parsing low-resource languages. The second study presents a method to automatically complete annotations from partially-annotated sentence data, with the help of Gibbs sampling. The algorithm significantly reduces the time required to annotate sentences for natural language processing, without a significant drop in annotation accuracy. The last study proposes a novel factor model for uncovering latent factors and exploring covariation among multiple outcomes of mixed types, including binary, count, and continuous data. Gibbs sampling is used to estimate model parameters. The algorithm successfully discovers correlation structures of mixed-type data in both simulated and real-word data.Operations Research and Industrial Engineerin

Transforming Projective Bilexical Dependency Grammars into Efficiently-Parsable CFGs with Unfold-Fold

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