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        <title>Whose Nickname is This? Recognizing Politicians from Their Aliases</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Whose Nickname is This? Recognizing Politicians from Their Aliases
%A Wang, Wei-Chung
%A Chen, Hung-Chen
%A Ji, Zhi-Kai
%A Hsiao, Hui-I
%A Chiu, Yu-Shian
%A Ku, Lun-Wei
%Y Han, Bo
%Y Ritter, Alan
%Y Derczynski, Leon
%Y Xu, Wei
%Y Baldwin, Tim
%S Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F wang-etal-2016-whose
%X 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.
%U https://aclanthology.org/W16-3910
%P 61-69

Markdown (Informal)

[Whose Nickname is This? Recognizing Politicians from Their Aliases](https://aclanthology.org/W16-3910) (Wang et al., WNUT 2016)

• Whose Nickname is This? Recognizing Politicians from Their Aliases (Wang et al., WNUT 2016)

ACL

• Wei-Chung Wang, Hung-Chen Chen, Zhi-Kai Ji, Hui-I Hsiao, Yu-Shian Chiu, and Lun-Wei Ku. 2016. Whose Nickname is This? Recognizing Politicians from Their Aliases. In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pages 61–69, Osaka, Japan. The COLING 2016 Organizing Committee.