Abstract

A wide variety of lexical resources have been created to allow automatic semantic processing of novel text. However, each resource has its own practical and theoretical idiosyncracies, making it difficult to combine the information from different resources. We discuss the form that these differences can take, and describe how we overcame some of them in creating a mapping between two important resources: Prop-Bank and VerbNet. Furthermore, we present experimental results that show that this mapping improves performance for PropBank-style semantic role labeling. Since PropBank was designed on a verb-by-verb basis, the argument labels Arg2- Arg5 get used for a wide variety of argument roles. As a result, it can be difficult for automatic classifiers to learn to distinguish these arguments. But by using the mapping that we have created between PropBank and VerbNet, we can train a classifier based on VerbNet argument labels, which are more consistent and therefore easier to learn. 1

Citations