Taking the trees produced by statistical parsers, we use tree cross-sections as features in a machine learning approach to determine which ones characterise non-native speaker errors.
we look at two types of parse tree substructure to use as features: horizontal slices of the trees-that is, characterising parse trees as sets of context-free grammar production rules-and the features schemas used in discriminative parse reranking.