Using LTAG based features in parse reranking

Abstract

We propose the use of Lexicalized Tree Adjoining Grammar (LTAG) as a source of features that are useful for reranking the output of a statistical parser. In this paper, we extend the notion of a tree kernel over arbitrary sub-trees of the parse to the derivation trees and derived trees provided by the LTAG formalism, and in addition, we extend the original definition of the tree kernel, making it more lexicalized and more compact. We use LTAG based features for the parse reranking task and obtain labeled recall and precision of 89.7%/90.0% on WSJ section 23 of Penn Treebank for sentences of length ≤ 100 words. Our results show that the use of LTAG based tree kernel gives rise to a 17% relative difference in f-score improvement over the use of a linear kernel without LTAG based features.