A Bayesian Model for Supervised Grammar Induction

In this paper، we show that the problem of grammar induction could be modeled as a combination of several model selection problems. We use the infinite generalization of a Bayesian model of cognition to solve each model selection problem in our grammar induction model. This Bayesian model is capable of solving model selection problems، consistent with human cognition. We also show that using the notion of history-based grammars will increase the number and decrease the complexity of model selection problems in our grammar induction model. This results in the induction of a better grammar which leads to 9. 1 points increase in F1 measure، for parsing the section 22 of Penn treebank in comparison with a similar model that does not use history-based grammar induction techniques.