Semantic Role Labeling with Neural Network Factors
Nicholas A. Fitzgerald, Oscar Täckström, Kuzman Ganchev, Dipanjan Das · 2015
We present a new method for semantic role labeling in which arguments and semantic roles are jointly embedded in a shared vector space for a given predicate.These embeddings belong to a neural network, whose output represents the potential functions of a graphical model designed for the SRL task.We consider both local and structured learning methods and obtain strong results on standard PropBank and FrameNet corpora with a straightforward product-of-experts model.We further show how the model can learn jointly from PropBank and FrameNet annotations to obtain additional improvements on the smaller FrameNet dataset.