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Online Graph Planarisation for Synchronous Parsing of Semantic and Syntactic Dependencies

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Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 1562-1566.

Abstract

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.Ivan Titov, James Henderson, Paola Merlo, Gabriele Musillo

Ivan Titov, James Henderson, Paola Merlo, and Gabriele Musillo. 2009. Online Graph Planarisation for Synchronous Parsing of Semantic and Syntactic Dependencies. In Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09. IJCAI, 1562–1566. https://ijcai.org/proceedings/2009/261
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