@InProceedings{botschen-moussellysergieh-gurevych:2017:RepL4NLP,
  author    = {Botschen, Teresa  and  Mousselly Sergieh, Hatem  and  Gurevych, Iryna},
  title     = {Prediction of Frame-to-Frame Relations in the FrameNet Hierarchy with Frame Embeddings},
  booktitle = {Proceedings of the 2nd Workshop on Representation Learning for NLP},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {146--156},
  abstract  = {Automatic completion of frame-to-frame (F2F) relations in the FrameNet (FN)
	hierarchy has received little attention, although they incorporate meta-level
	commonsense knowledge and are used in downstream approaches. We address the
	problem of sparsely annotated F2F relations. First, we examine whether the
	manually defined F2F relations emerge from text by learning text-based frame
	embeddings. Our analysis reveals insights about the difficulty of
	reconstructing F2F relations purely from text. Second, we present different
	systems for predicting F2F relations; our best-performing one uses the FN
	hierarchy to train on and to ground embeddings in. A comparison of systems and
	embeddings exposes the crucial influence of knowledge-based embeddings to a
	system’s performance in predicting F2F relations.},
  url       = {http://www.aclweb.org/anthology/W17-2618}
}