@InProceedings{liang-xu-zhao:2017:RepL4NLP,
  author    = {Liang, Dongyun  and  Xu, Weiran  and  Zhao, Yinge},
  title     = {Combining Word-Level and Character-Level Representations for Relation Classification of Informal Text},
  booktitle = {Proceedings of the 2nd Workshop on Representation Learning for NLP},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {43--47},
  abstract  = {Word representation models have achieved great success in natural language
	processing tasks, such as relation classification. However, it does not always
	work on informal text, and the morphemes of some misspelling  words may carry
	important short-distance semantic information.
	We propose a hybrid model, combining the merits of word-level and
	character-level representations to learn better representations on informal
	text. 
	Experiments on two dataset of relation classification, SemEval-2010 Task8 and a
	large-scale one we compile from informal text, show that our model achieves a
	competitive result in the former and state-of-the-art with the other.},
  url       = {http://www.aclweb.org/anthology/W17-2606}
}