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- W1989115577 abstract "We formulate dependency parsing as a graphical model with the novel ingredient of global constraints. We show how to apply loopy belief propagation (BP), a simple and effective tool for approximate learning and inference. As a parsing algorithm, BP is both asymptotically and empirically efficient. Even with second-order features or latent variables, which would make exact parsing considerably slower or NP-hard, BP needs only O(n3) time with a small constant factor. Furthermore, such features significantly improve parse accuracy over exact first-order methods. Incorporating additional features would increase the runtime additively rather than multiplicatively." @default.
- W1989115577 created "2016-06-24" @default.
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- W1989115577 date "2008-01-01" @default.
- W1989115577 modified "2023-09-23" @default.
- W1989115577 title "Dependency parsing by belief propagation" @default.
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- W1989115577 doi "https://doi.org/10.3115/1613715.1613737" @default.
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