At Clippers Tuesday Lifeng Jin will present:
Unsupervised Grammar Induction with Depth-bounded PCFG
There has been recent interest in applying cognitively or empirically motivated bounds on recursion depth to limit the search space of grammar induction models. In this talk, I will present a Bayesian grammar induction model which extends this depth-bounding approach to probabilistic context-free grammar induction (DB-PCFG), which has a smaller parameter space than hierarchic sequence models, and therefore more fully exploits the space reductions of depth-bounding.
Results for this model on grammar acquisition from a synthetic dataset and transcribed child-directed speech exceed those of other models when evaluated on parse accuracy. Moreover, grammars acquired from this model demonstrate a consistent use of category labels, something which has not been demonstrated by other acquisition models.