
Learning to Transform Linguistic Graphs 


We argue in favor of the the use of labeled directed graph to represent various types of linguistic structures, and illustrate how this allows one to view NLP tasks as graph transformations. We present a general method for learning such transformations from an annotated corpus and describe experiments with two applications of the method: identification of non-local depenencies (using Penn Treebank data) and semantic role labeling (using Proposition Bank data).

