@InProceedings{feng-EtAl:2017:starSEM,
  author    = {Feng, Yukun  and  Yu, Dong  and  Xu, Jian  and  Liu, Chunhua},
  title     = {Semantic Frame Labeling with Target-based Neural Model},
  booktitle = {Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {91--96},
  abstract  = {This paper explores the automatic learning of distributed representations of
	the target's context for semantic frame labeling with target-based neural
	model. We constrain the whole sentence as the model's input without feature
	extraction from the sentence. This is different from many previous works in
	which local feature extraction of the targets is widely used. This constraint
	makes the task harder, especially with long sentences, but also makes our model
	easily applicable to a range of resources and other similar tasks. We evaluate
	our model on several resources and get the state-of-the-art result on subtask 2
	of SemEval 2015 task 15. Finally, we extend the task to word-sense
	disambiguation task and we also achieve a strong result in comparison to
	state-of-the-art work.},
  url       = {http://www.aclweb.org/anthology/S17-1010}
}