@inproceedings{chen-etal-2018-emojiit,
title = "{E}moji{I}t at {S}em{E}val-2018 Task 2: An Effective Attention-Based Recurrent Neural Network Model for Emoji Prediction with Characters Gated Words",
author = "Chen, Shiyun and
Wang, Maoquan and
He, Liang",
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/S18-1066",
doi = "10.18653/v1/S18-1066",
pages = "423--427",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T EmojiIt at SemEval-2018 Task 2: An Effective Attention-Based Recurrent Neural Network Model for Emoji Prediction with Characters Gated Words
%A Chen, Shiyun
%A Wang, Maoquan
%A He, Liang
%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-emojiit
%X 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.
%R 10.18653/v1/S18-1066
%U https://aclanthology.org/S18-1066
%U https://doi.org/10.18653/v1/S18-1066
%P 423-427
Markdown (Informal)
[EmojiIt at SemEval-2018 Task 2: An Effective Attention-Based Recurrent Neural Network Model for Emoji Prediction with Characters Gated Words](https://aclanthology.org/S18-1066) (Chen et al., SemEval 2018)
ACL