@InProceedings{qin-zhang-zhao:2016:COLING,
  author    = {Qin, Lianhui  and  Zhang, Zhisong  and  Zhao, Hai},
  title     = {Implicit Discourse Relation Recognition with Context-aware Character-enhanced Embeddings},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {1914--1924},
  abstract  = {For the task of implicit discourse relation recognition, traditional models
	utilizing manual features can suffer from data sparsity problem. Neural models
	provide a solution with distributed representations, which could encode the
	latent semantic information, and are suitable for recognizing semantic
	relations between argument pairs. However, conventional vector representations
	usually adopt embeddings at the word level and cannot well handle the rare word
	problem without carefully considering morphological information at character
	level. Moreover, embeddings are assigned to individual words independently,
	which lacks of the crucial contextual information. This paper proposes a neural
	model utilizing context-aware character-enhanced embeddings to alleviate the
	drawbacks of the current word level representation. Our experiments show that
	the enhanced embeddings work well and the proposed model obtains
	state-of-the-art results.},
  url       = {http://aclweb.org/anthology/C16-1180}
}