AAAI Publications, Twenty-First International Joint Conference on Artificial Intelligence

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Online Graph Planarisation for Synchronous Parsing of Semantic and Syntactic Dependencies
Ivan Titov, James Henderson, Paola Merlo, Gabriele Musillo

Last modified: 2009-06-26


This paper investigates a generative history-based parsing model that synchronises the derivation of non-planar graphs representing semantic dependencies with the derivation of dependency trees representing syntactic structures. To process non-planarity online, the semantic transition-based parser uses a new technique to dynamically reorder nodes during the derivation. While the synchronised derivations allow different structures to be built for the semantic non-planar graphs and syntactic dependency trees, useful statistical dependencies between these structures are modeled using latent variables. The resulting synchronous parser achieves competitive performance on the CoNLL-2008 shared task, achieving relative error reduction of 12% in semantic F score over previously proposed synchronous models that cannot process non-planarity online.


Dependency Parsing; Semantic Role Labeling; Non-Projective Parsing; Latent Variables

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