A Latent Variable Model of Synchronous Parsing for Syntactic and Semantic Dependencies

James Henderson, Paola Merlo, Gabriele Musillo, Ivan Titov

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

Abstract / Description of output

We propose a solution to the challenge of the CoNLL 2008 shared task that uses a generative history-based latent variable model to predict the most likely derivation of a synchronous dependency parser for both syntactic and semantic dependencies. The submitted model yields 79.1% macro-average F1 performance, for the joint task, 86.9% syntactic dependencies LAS and 71.0% semantic dependencies F1. A larger model trained after the deadline achieves 80.5% macro-average F1, 87.6% syntactic dependencies LAS, and 73.1% semantic dependencies F1.
Original languageEnglish
Title of host publicationProceedings of the Twelfth Conference on Computational Natural Language Learning
Place of PublicationStroudsburg, PA, USA
PublisherAssociation for Computational Linguistics
Pages178-182
Number of pages5
ISBN (Print)978-1-905593-48-4
Publication statusPublished - Aug 2008

Publication series

NameCoNLL '08
PublisherAssociation for Computational Linguistics

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