@inproceedings{wu-etal-2018-thu-ngn-semeval,
title = "{THU}{\_}{NGN} at {S}em{E}val-2018 Task 2: Residual {CNN}-{LSTM} Network with Attention for {E}nglish Emoji Prediction",
author = "Wu, Chuhan and
Wu, Fangzhao and
Wu, Sixing and
Yuan, Zhigang and
Liu, Junxin and
Huang, Yongfeng",
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/S18-1063",
doi = "10.18653/v1/S18-1063",
pages = "410--414",
abstract = "Emojis are widely used by social media and social network users when posting their messages. It is important to study the relationships between messages and emojis. Thus, in SemEval-2018 Task 2 an interesting and challenging task is proposed, i.e., predicting which emojis are evoked by text-based tweets. We propose a residual CNN-LSTM with attention (\textbf{RCLA}) model for this task. Our model combines CNN and LSTM layers to capture both local and long-range contextual information for tweet representation. In addition, attention mechanism is used to select important components. Besides, residual connection is applied to CNN layers to facilitate the training of neural networks. We also incorporated additional features such as POS tags and sentiment features extracted from lexicons. Our model achieved 30.25{\%} macro-averaged F-score in the first subtask (i.e., emoji prediction in English), ranking 7th out of 48 participants.",
}
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<abstract>Emojis are widely used by social media and social network users when posting their messages. It is important to study the relationships between messages and emojis. Thus, in SemEval-2018 Task 2 an interesting and challenging task is proposed, i.e., predicting which emojis are evoked by text-based tweets. We propose a residual CNN-LSTM with attention (RCLA) model for this task. Our model combines CNN and LSTM layers to capture both local and long-range contextual information for tweet representation. In addition, attention mechanism is used to select important components. Besides, residual connection is applied to CNN layers to facilitate the training of neural networks. We also incorporated additional features such as POS tags and sentiment features extracted from lexicons. Our model achieved 30.25% macro-averaged F-score in the first subtask (i.e., emoji prediction in English), ranking 7th out of 48 participants.</abstract>
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%0 Conference Proceedings
%T THU_NGN at SemEval-2018 Task 2: Residual CNN-LSTM Network with Attention for English Emoji Prediction
%A Wu, Chuhan
%A Wu, Fangzhao
%A Wu, Sixing
%A Yuan, Zhigang
%A Liu, Junxin
%A Huang, Yongfeng
%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 wu-etal-2018-thu-ngn-semeval
%X Emojis are widely used by social media and social network users when posting their messages. It is important to study the relationships between messages and emojis. Thus, in SemEval-2018 Task 2 an interesting and challenging task is proposed, i.e., predicting which emojis are evoked by text-based tweets. We propose a residual CNN-LSTM with attention (RCLA) model for this task. Our model combines CNN and LSTM layers to capture both local and long-range contextual information for tweet representation. In addition, attention mechanism is used to select important components. Besides, residual connection is applied to CNN layers to facilitate the training of neural networks. We also incorporated additional features such as POS tags and sentiment features extracted from lexicons. Our model achieved 30.25% macro-averaged F-score in the first subtask (i.e., emoji prediction in English), ranking 7th out of 48 participants.
%R 10.18653/v1/S18-1063
%U https://aclanthology.org/S18-1063
%U https://doi.org/10.18653/v1/S18-1063
%P 410-414
Markdown (Informal)
[THU_NGN at SemEval-2018 Task 2: Residual CNN-LSTM Network with Attention for English Emoji Prediction](https://aclanthology.org/S18-1063) (Wu et al., SemEval 2018)
ACL