@InProceedings{chen-EtAl:2018:S18-1,
  author    = {Chen, Jing  and  Yang, Dechuan  and  Li, Xilian  and  Chen, Wei  and  Wang, Tengjiao},
  title     = {Peperomia at SemEval-2018 Task 2: Vector Similarity Based Approach for Emoji Prediction},
  booktitle = {Proceedings of The 12th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {428--432},
  abstract  = {This paper describes our participation in Se- mEval 2018 Task 2: Multilingual Emoji Pre- diction, in which participants are asked to pre- dict a tweet’s most associated emoji from 20 emojis. Instead of regarding it as a 20-class classification problem we regard it as a text similarity problem. We propose a vector sim- ilarity based approach for this task. First the distributed representation (tweet vector) for each tweet is generated, then the similarity be- tween this tweet vector and each emoji’s em- bedding is evaluated. The most similar emoji is chosen as the predicted label. Experimental results show that our approach performs com- parably with the classification approach and shows its advantage in classifying emojis with similar semantic meaning.},
  url       = {http://www.aclweb.org/anthology/S18-1067}
}