Judging Grammaticality with Tree Substitution Grammar Derivations
Matt Post · 2011
In this paper, we show that local features com-puted from the derivations of tree substitution grammars — such as the identify of particu-lar fragments, and a count of large and small fragments — are useful in binary grammatical classification tasks. Such features outperform n-gram features and various model scores by a wide margin. Although they fall short of the performance of the hand-crafted feature set of Charniak and Johnson (2005) developed for parse tree reranking, they do so with an order of magnitude fewer features. Further-more, since the TSGs employed are learned in a Bayesian setting, the use of their deriva-tions can be viewed as the automatic discov-ery of tree patterns useful for classification. On the BLLIP dataset, we achieve an accuracy of 89.9 % in discriminating between grammat-ical text and samples from an n-gram language model. 1