EmojiIt at SemEval-2018 Task 2: An Effective Attention-Based Recurrent Neural Network Model for Emoji Prediction with Characters Gated Words

Shiyun Chen, Maoquan Wang, Liang He


Abstract
This paper presents our single model to Subtask 1 of SemEval 2018 Task 2: Emoji Prediction in English. In order to predict the emoji that may be contained in a tweet, the basic model we use is an attention-based recurrent neural network which has achieved satisfactory performs in Natural Language processing. Considering the text comes from social media, it contains many discrepant abbreviations and online terms, we also combine word-level and character-level word vector embedding to better handling the words not appear in the vocabulary. Our single model1 achieved 29.50% Macro F-score in test data and ranks 9th among 48 teams.
Anthology ID:
S18-1066
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:
423–427
Language:
URL:
https://www.aclweb.org/anthology/S18-1066
DOI:
10.18653/v1/S18-1066
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1066.pdf
Note:
 S18-1066.Notes.pdf