Grammar Induction & Language Evolution

Recent research on unsupervised grammar induction has focused on inducing accurate bracketing of sentences. Here we present an efficient, Bayesian algorithm for the unsupervised induction of syntactic categories from such bracketed text. Our model gives state-of-the-art results on this task, using gold-standard bracketing, outperforming the recent semi-supervised approach of (Haghighi and Klein, 2006), obtaining an F1 of 76.8% (when appropriately relabeled). Our algorithm assigns comparable likelihood to unseen text as the treebank PCFG. Finally, we discuss the metrics used and linguistic relevance of the results.

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