Incremental Bayesian Networks for Structure Prediction

Ivan Titov, James Henderson

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

Abstract

We propose a class of graphical models appropriate for structure prediction problems where the model structure is a function of the output structure. Incremental Sigmoid Belief Networks (ISBNs) avoid the need to sum over the possible model structures by using directed arcs and incrementally specifying the model structure. Exact inference in such directed models is not tractable, but we derive two efficient approximations based on mean field methods, which prove effective in artificial experiments. We then demonstrate their effectiveness on a benchmark natural language parsing task, where they achieve state-of-the-art accuracy. Also, the model which is a closer approximation to an ISBN has better parsing accuracy, suggesting that ISBNs are an appropriate abstract model of structure prediction tasks.
Original languageEnglish
Title of host publicationProceedings of the 24th International Conference on Machine Learning
Place of PublicationNew York, NY, USA
PublisherACM
Pages887-894
Number of pages8
ISBN (Print)978-1-59593-793-3
DOIs
Publication statusPublished - 2007

Publication series

NameICML '07
PublisherACM

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