@InProceedings{hartmann-EtAl:2017:EACLlong,
  author    = {Hartmann, Silvana  and  Kuznetsov, Ilia  and  Martin, Teresa  and  Gurevych, Iryna},
  title     = {Out-of-domain FrameNet Semantic Role Labeling},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {471--482},
  abstract  = {Domain dependence of NLP systems is one of the major obstacles to their
	application in large-scale text analysis, also restricting the applicability of
	FrameNet semantic role labeling (SRL) systems. Yet, current FrameNet SRL
	systems are still only evaluated on a single in-domain test set. For the first
	time, we study the domain dependence of FrameNet SRL on a wide range of
	benchmark sets. We create a novel test set for FrameNet SRL based on
	user-generated web text and find that the major bottleneck for out-of-domain
	FrameNet SRL is the frame identification step. To address this problem, we
	develop a simple, yet efficient
	system based on distributed word representations. Our system closely approaches
	the state-of-the-art in-domain while outperforming the best available frame
	identification system out-of-domain. We publish our system and test data for
	research purposes.},
  url       = {http://www.aclweb.org/anthology/E17-1045}
}