Online Graph Planarisation for Synchronous Parsing of Semantic and Syntactic Dependencies

Ivan Titov, James Henderson, Paola Merlo, Gabriele Musillo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationIJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence, Pasadena, California, USA, July 11-17, 2009
PublisherIJCAI Inc
Number of pages6
Publication statusPublished - 2009


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