Year: 2010
Type: dissertation
Abstract: Achieving a greater understanding of natural language syntax and parsing is a critical step in producing useful natural language processing systems.In this thesis, we focus on the formalism of dependency grammar as it allows one to model important headmodifier relationships with a minimum of extraneous structure.Recent research in dependency parsing has highlighted the discriminative structured prediction framework (McDonald et al., 2005a;Carreras, 2007;Suzuki et al., 2009), which is characterized by two advantages: first, the availability of powerful discriminative learning algorithms like log-linear and max-margin models (Lafferty et al., 2001;Taskar et al., 2003), and second, the ability to use arbitrarily-defined feature representations.This thesis explores three advances in the field of discriminative dependency parsing.First, we show that the classic Matrix-Tree Theorem (Kirchhoff, 1847; Tutte, 1984) can be applied to the problem of non-projective dependency parsing, enabling both log-linear and max-margin parameter estimation in this setting.Second, we present novel third-order dependency parsing algorithms that extend the amount of context available to discriminative parsers while retaining computational complexity equivalent to existing second-order parsers.Finally, we describe a simple but effective method for augmenting the features of a dependency parser with information derived from standard clustering algorithms; our semi-supervised approach is able to deliver consistent benefits regardless of the amount of available training data.
Authors: Michael Collins, Terry Koo
Institution: Massachusetts Institute of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2107583497