THU_NGN at SemEval-2018 Task 2: Residual CNN-LSTM Network with Attention for English Emoji Prediction

Chuhan Wu, Fangzhao Wu, Sixing Wu, Zhigang Yuan, Junxin Liu, Yongfeng Huang


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.
Anthology ID:
S18-1063
Volume:
Proceedings of The 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
410–414
Language:
URL:
https://www.aclweb.org/anthology/S18-1063
DOI:
10.18653/v1/S18-1063
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1063.pdf