Exploration of the LTAG-Spinal Formalism and Treebank for Semantic Role Labeling

LTAG-spinal is a novel variant of traditional Lexicalized Tree Adjoining Grammar (LTAG) introduced by (Shen, 2006). The LTAG-spinal Treebank (Shen et al., 2008) combines elementary trees extracted from the Penn Treebank with Propbank annotation. In this paper, we present a semantic role labeling (SRL) system based on this new resource and provide an experimental comparison with CCGBank and a state-of-the-art SRL system based on Treebank phrase-structure trees. Deep linguistic information such as predicateargument relationships that are either implicit or absent from the original Penn Treebank are made explicit and accessible in the LTAG-spinal Treebank, which we show to be a useful resource for semantic role labeling.