@InProceedings{he-fancellu-webber:2017:SemBEaR,
  author    = {He, Hangfeng  and  Fancellu, Federico  and  Webber, Bonnie},
  title     = {Neural Networks for Negation Cue Detection in Chinese},
  booktitle = {Proceedings of the Workshop Computational Semantics Beyond Events and Roles},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {59--63},
  abstract  = {Negation cue detection involves identifying the span inherently expressing
	negation in a negative sentence. In Chinese, negative cue detection is
	complicated by morphological proprieties of the language. Previous work has
	shown that negative cue detection in Chinese can benefit from specific lexical
	and morphemic features, as well as cross-lingual information. We show here that
	they are not necessary: A bi-directional LSTM can perform equally well, with
	minimal feature engineering. In particular, the use of a character-based model
	allows us to capture characteristics of negation cues in Chinese using
	word-embedding information only. Not only does our model performs on par with
	previous work, further error analysis clarifies what problems remain to be
	addressed.},
  url       = {http://www.aclweb.org/anthology/W17-1809}
}