%0 Conference Proceedings %T Out-of-domain FrameNet Semantic Role Labeling %A Hartmann, Silvana %A Kuznetsov, Ilia %A Martin, Teresa %A Gurevych, Iryna %Y Lapata, Mirella %Y Blunsom, Phil %Y Koller, Alexander %S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers %D 2017 %8 April %I Association for Computational Linguistics %C Valencia, Spain %F hartmann-etal-2017-domain %X 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. %U https://aclanthology.org/E17-1045 %P 471-482