Judging grammaticality with count-induced tree substitution grammars

Authors:
Francis Ferraro;Matt Post;Benjamin Van Durme
Affiliations:
Johns Hopkins University;Johns Hopkins University;Johns Hopkins University
Venue:
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
Year:
2012

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Abstract

Prior work has shown the utility of syntactic tree fragments as features in judging the grammaticality of text. To date such fragments have been extracted from derivations of Bayesian-induced Tree Substitution Grammars (TSGs). Evaluating on discriminative coarse and fine grammaticality classification tasks, we show that a simple, deterministic, count-based approach to fragment identification performs on par with the more complicated grammars of Post (2011). This represents a significant reduction in complexity for those interested in the use of such fragments in the development of systems for the educational domain.