Jump to Content

Semantic Role Labeling with Neural Network Factors

Oscar Täckström
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP '15), Association for Computational Linguistics


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.