PropBank (2005) › 2005: PropBank

In the mid-2000s, researchers at the University of Pennsylvania began adding semantic role labels to the Penn Treebank's syntactic annotations. The resulting Proposition Bank, or PropBank, was developed under the leadership of Martha Palmer, Daniel Gildea, and Paul Kingsbury. Released in 2005, it paired a large semantic annotation resource with an established syntactic treebank, giving statistical systems training data for identifying "who did what to whom" while retaining the Penn Treebank's syntactic structure.

By 2005, the Penn Treebank was a standard resource for training statistical parsers, but syntax alone did not encode the roles that participants played in an event. FrameNet had demonstrated semantic role annotation with named roles such as Buyer and Seller, though its detailed frame-based approach covered a limited vocabulary. PropBank pursued broader coverage with numbered arguments (Arg0, Arg1, Arg2, and so on) rather than named frame elements. This simpler scheme allowed more predicates to be annotated on the existing Penn Treebank trees, placing syntactic and semantic information in the same resource.

PropBank's predicate-argument annotations made it possible to train and evaluate statistical systems for semantic role labeling (SRL). The resulting dataset helped turn SRL into a repeatable benchmark and showed that semantic annotation could be produced at a scale useful for machine learning.

Later semantic resources adopted PropBank's compatibility with existing syntax and its use of frame files for verb-specific roles. They also emphasized annotation consistency. PropBank supplied data for the CoNLL shared tasks and informed later projects such as OntoNotes. Its coverage made statistical evaluation possible across a sizeable portion of the vocabulary.