Language Modeling with Tree Substitution Grammars

author: Matt Post, University of Rochester
published: Jan. 19, 2010,   recorded: December 2009,   views: 6171


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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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