 A structure assignment task that can be viewed as a step towards semantic interpretation is semantic role labeling (Palmer et al. 2010). The goal is to assign thematic roles such as agent, theme, recipient, etc. to core phrases or phrasal heads in relation to verbs (and perhaps other complement-taking words). While this can be approached as a sequence labeling problem, experimental evidence shows that computing parse trees and using resulting structural features for role assignment (or jointly computing parse trees and roles) improves precision. A frequently used training corpus for such work is PropBank, a version of the Penn Treebank annotated with “neutral” roles arg0, arg1, arg2, etc.