@InProceedings{becker-EtAl:2017:starSEM,
  author    = {Becker, Maria  and  Staniek, Michael  and  Nastase, Vivi  and  Palmer, Alexis  and  Frank, Anette},
  title     = {Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention},
  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     = {230--240},
  abstract  = {Detecting aspectual properties of clauses in the form of situation entity types
	has been shown to depend on a combination of syntactic-semantic and contextual
	features. We explore this task in a deep-learning framework, where tuned word
	representations capture lexical, syntactic and semantic features. We introduce
	an attention mechanism that pinpoints relevant context not only for the current
	instance, but also for the larger context. Apart from implicitly capturing task
	relevant features, the advantage of our neural model is that it avoids the need
	to reproduce linguistic features for other languages and is thus more easily
	transferable. We present experiments for English and German that achieve
	competitive performance. We present a novel take on modeling and exploiting
	genre information and showcase the adaptation of our system from one language
	to another.},
  url       = {http://www.aclweb.org/anthology/S17-1027}
}