Volume 9, Issue 1 (9-2012)                   JSDP 2012, 9(1): 19-34 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Faili H, Ghader H, Morteza Analoui M. A Bayesian Model for Supervised Grammar Induction. JSDP 2012; 9 (1) :19-34
URL: http://jsdp.rcisp.ac.ir/article-1-696-en.html
A Bayesian Model for Supervised Grammar Induction
Heshaam Faili * , Hamidreza Ghader , Morteza Morteza Analoui
Abstract:   (3765 Views)

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.

 
Keywords: Bayesian model of cognition, tree substitution grammars, Dirichlet process, Chinese restaurant process, history-based grammars
Full-Text [PDF 2674 kb]   (1025 Downloads)    
Type of Study: Research | Subject: Paper
Received: 2012/09/21 | Accepted: 2018/02/19 | Published: 2018/02/19 | ePublished: 2018/02/19
Add your comments about this article : Your username or Email:
CAPTCHA
Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2015 All Rights Reserved | Signal and Data Processing