Semantic Role Labeling with Neural Network Factors
Venue
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP '15), Association for Computational Linguistics
Publication Year
2015
Authors
Nicholas FitzGerald, Oscar Täckström, Kuzman Ganchev, Dipanjan Das
BibTeX
Abstract
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.
