@inproceedings{kim-etal-2018-attnconvnet,
title = "{A}ttn{C}onvnet at {S}em{E}val-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion Classification",
author = "Kim, Yanghoon and
Lee, Hwanhee and
Jung, Kyomin",
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/S18-1019",
doi = "10.18653/v1/S18-1019",
pages = "141--145",
abstract = "In this paper, we propose an attention-based classifier that predicts multiple emotions of a given sentence. Our model imitates human{'}s two-step procedure of sentence understanding and it can effectively represent and classify sentences. With emoji-to-meaning preprocessing and extra lexicon utilization, we further improve the model performance. We train and evaluate our model with data provided by SemEval-2018 task 1-5, each sentence of which has several labels among 11 given emotions. Our model achieves 5th/1st rank in English/Spanish respectively.",
}
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<abstract>In this paper, we propose an attention-based classifier that predicts multiple emotions of a given sentence. Our model imitates human’s two-step procedure of sentence understanding and it can effectively represent and classify sentences. With emoji-to-meaning preprocessing and extra lexicon utilization, we further improve the model performance. We train and evaluate our model with data provided by SemEval-2018 task 1-5, each sentence of which has several labels among 11 given emotions. Our model achieves 5th/1st rank in English/Spanish respectively.</abstract>
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%0 Conference Proceedings
%T AttnConvnet at SemEval-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion Classification
%A Kim, Yanghoon
%A Lee, Hwanhee
%A Jung, Kyomin
%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 kim-etal-2018-attnconvnet
%X In this paper, we propose an attention-based classifier that predicts multiple emotions of a given sentence. Our model imitates human’s two-step procedure of sentence understanding and it can effectively represent and classify sentences. With emoji-to-meaning preprocessing and extra lexicon utilization, we further improve the model performance. We train and evaluate our model with data provided by SemEval-2018 task 1-5, each sentence of which has several labels among 11 given emotions. Our model achieves 5th/1st rank in English/Spanish respectively.
%R 10.18653/v1/S18-1019
%U https://aclanthology.org/S18-1019
%U https://doi.org/10.18653/v1/S18-1019
%P 141-145
Markdown (Informal)
[AttnConvnet at SemEval-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion Classification](https://aclanthology.org/S18-1019) (Kim et al., SemEval 2018)
ACL