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Language Modeling with Tree Substitution Grammars
This video was recorded at NIPS Workshops, Whistler 2009. We show that a tree substitution grammar (TSG) induced with a collapsed Gibbs sampler results in lower perplexity on test data than both a standard context-free grammar and other heuristically trained TSGs, suggesting that it is better suited to language modeling. Training a more complicated bilexical parsing model across TSG derivations shows further (though nuanced) improvement. We conduct analysis and point to future areas of research using TSGs as language models.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
