@inproceedings{wang-pedersen-2018-umdsub,
title = "{UMDS}ub at {S}em{E}val-2018 Task 2: Multilingual Emoji Prediction Multi-channel Convolutional Neural Network on Subword Embedding",
author = "Wang, Zhenduo and
Pedersen, Ted",
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/S18-1060",
doi = "10.18653/v1/S18-1060",
pages = "395--399",
abstract = "This paper describes the UMDSub system that participated in Task 2 of SemEval-2018. We developed a system that predicts an emoji given the raw text in a English tweet. The system is a Multi-channel Convolutional Neural Network based on subword embeddings for the representation of tweets. This model improves on character or word based methods by about 2{\%}. Our system placed 21st of 48 participating systems in the official evaluation.",
}
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<abstract>This paper describes the UMDSub system that participated in Task 2 of SemEval-2018. We developed a system that predicts an emoji given the raw text in a English tweet. The system is a Multi-channel Convolutional Neural Network based on subword embeddings for the representation of tweets. This model improves on character or word based methods by about 2%. Our system placed 21st of 48 participating systems in the official evaluation.</abstract>
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%0 Conference Proceedings
%T UMDSub at SemEval-2018 Task 2: Multilingual Emoji Prediction Multi-channel Convolutional Neural Network on Subword Embedding
%A Wang, Zhenduo
%A Pedersen, Ted
%S Proceedings of The 12th International Workshop on Semantic Evaluation
%D 2018
%8 jun
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F wang-pedersen-2018-umdsub
%X This paper describes the UMDSub system that participated in Task 2 of SemEval-2018. We developed a system that predicts an emoji given the raw text in a English tweet. The system is a Multi-channel Convolutional Neural Network based on subword embeddings for the representation of tweets. This model improves on character or word based methods by about 2%. Our system placed 21st of 48 participating systems in the official evaluation.
%R 10.18653/v1/S18-1060
%U https://aclanthology.org/S18-1060
%U https://doi.org/10.18653/v1/S18-1060
%P 395-399
Markdown (Informal)
[UMDSub at SemEval-2018 Task 2: Multilingual Emoji Prediction Multi-channel Convolutional Neural Network on Subword Embedding](https://aclanthology.org/S18-1060) (Wang & Pedersen, SemEval 2018)
ACL