Prototype-Driven Grammar Induction

We investigate prototype-driven learning for primarily unsupervised grammar induction. Prior knowledge is specified declaratively, by providing a few canonical examples of each target phrase type. This sparse prototype information is then propagated across a corpus using distributional similarity features, which augment an otherwise standard PCFG model. We show that distributional features are effective at distinguishing bracket labels, but not determining bracket locations. To improve the quality of the induced trees, we combine our PCFG induction with the CCM model of Klein and Manning (2002), which has complementary stengths: it identifies brackets but does not label them. Using only a handful of prototypes, we show substantial improvements over naive PCFG induction for English and Chinese grammar induction.

• Type: Conference
• Year: 2006
• Where: ACL
• Authors: Aria Haghighi, Dan Klein
• Keywords: ACL 2006, ACL 2007, Grammar Induction, PCFG Induction, Unsupervised Grammar Induction