Unsupervised Learning of Probabilistic Context-Free Grammar using Iterative Biclustering

Authors:
Kewei Tu;Vasant Honavar
Affiliations:
Department of Computer Science, Iowa State University, Ames, USA IA 50011;Department of Computer Science, Iowa State University, Ames, USA IA 50011
Venue:
ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
Year:
2008

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Abstract

This paper presents PCFG-BCL, an unsupervised algorithm that learns a probabilistic context-free grammar (PCFG) from positive samples. The algorithm acquires rules of an unknown PCFG through iterative biclustering of bigrams in the training corpus. Our analysis shows that this procedure uses a greedy approach to adding rules such that each set of rules that is added to the grammar results in the largest increase in the posterior of the grammar given the training corpus. Results of our experiments on several benchmark datasets show that PCFG-BCL is competitive with existing methods for unsupervised CFG learning.