@InProceedings{wang-EtAl:2016:WNUT,
  author    = {Wang, Wei-Chung  and  Chen, Hung-Chen  and  Ji, Zhi-Kai  and  Hsiao, Hui-I  and  Chiu, Yu-Shian  and  Ku, Lun-Wei},
  title     = {Whose Nickname is This? Recognizing Politicians from Their Aliases},
  booktitle = {Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {61--69},
  abstract  = {Using aliases to refer to public figures is one way to make fun of people, to
	express sarcasm, or even to sidestep legal issues when expressing opinions on
	social media. However, linking an alias back to the real name is difficult, as
	it entails phonemic, graphemic, and semantic challenges. In this paper, we
	propose a phonemic-based approach and inject semantic information to align
	aliases with politicians' Chinese formal names. The proposed approach creates
	an HMM model for each name to model its phonemes and takes into account
	document-level pairwise mutual information to capture the semantic relations to
	the alias. In this work we also introduce two new datasets consisting of 167
	phonemic pairs and 279 mixed pairs of aliases and formal names. Experimental
	results show that the proposed approach models both phonemic and semantic
	information and outperforms previous work on both the phonemic and mixed
	datasets with the best top-1 accuracies of 0.78 and 0.59 respectively.},
  url       = {http://aclweb.org/anthology/W16-3910}
}