@inproceedings{chen-etal-2018-peperomia,
title = "Peperomia at {S}em{E}val-2018 Task 2: Vector Similarity Based Approach for Emoji Prediction",
author = "Chen, Jing and
Yang, Dechuan and
Li, Xilian and
Chen, Wei and
Wang, Tengjiao",
editor = "Apidianaki, Marianna and
Mohammad, Saif M. and
May, Jonathan and
Shutova, Ekaterina and
Bethard, Steven and
Carpuat, Marine",
booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-1067",
doi = "10.18653/v1/S18-1067",
pages = "428--432",
abstract = "This paper describes our participation in SemEval 2018 Task 2: Multilingual Emoji Prediction, in which participants are asked to predict 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 similarity based approach for this task. First the distributed representation (tweet vector) for each tweet is generated, then the similarity between this tweet vector and each emoji{'}s embedding is evaluated. The most similar emoji is chosen as the predicted label. Experimental results show that our approach performs comparably with the classification approach and shows its advantage in classifying emojis with similar semantic meaning.",
}
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<abstract>This paper describes our participation in SemEval 2018 Task 2: Multilingual Emoji Prediction, in which participants are asked to predict 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 similarity based approach for this task. First the distributed representation (tweet vector) for each tweet is generated, then the similarity between this tweet vector and each emoji’s embedding is evaluated. The most similar emoji is chosen as the predicted label. Experimental results show that our approach performs comparably with the classification approach and shows its advantage in classifying emojis with similar semantic meaning.</abstract>
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%0 Conference Proceedings
%T Peperomia at SemEval-2018 Task 2: Vector Similarity Based Approach for Emoji Prediction
%A Chen, Jing
%A Yang, Dechuan
%A Li, Xilian
%A Chen, Wei
%A Wang, Tengjiao
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F chen-etal-2018-peperomia
%X This paper describes our participation in SemEval 2018 Task 2: Multilingual Emoji Prediction, in which participants are asked to predict 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 similarity based approach for this task. First the distributed representation (tweet vector) for each tweet is generated, then the similarity between this tweet vector and each emoji’s embedding is evaluated. The most similar emoji is chosen as the predicted label. Experimental results show that our approach performs comparably with the classification approach and shows its advantage in classifying emojis with similar semantic meaning.
%R 10.18653/v1/S18-1067
%U https://aclanthology.org/S18-1067
%U https://doi.org/10.18653/v1/S18-1067
%P 428-432
Markdown (Informal)
[Peperomia at SemEval-2018 Task 2: Vector Similarity Based Approach for Emoji Prediction](https://aclanthology.org/S18-1067) (Chen et al., SemEval 2018)
ACL