@inproceedings{zhang-etal-2018-nlpzzx,
title = "{NLPZZX} at {S}em{E}val-2018 Task 1: Using Ensemble Method for Emotion and Sentiment Intensity Determination",
author = "Zhang, Zhengxin and
Zhou, Qimin and
Wu, Hao",
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/S18-1015",
doi = "10.18653/v1/S18-1015",
pages = "116--122",
abstract = "In this paper, we put forward a system that competed at SemEval-2018 Task 1: {``}Affect in Tweets{''}. Our system uses a simple yet effective ensemble method which combines several neural network components. We participate in two subtasks for English tweets: EI-reg and V-reg. For two subtasks, different combinations of neural components are examined. For EI-reg, our system achieves an accuracy of 0.727 in Pearson Correlation Coefficient (all instances) and an accuracy of 0.555 in Pearson Correlation Coefficient (0.5-1). For V-reg, the achieved accuracy scores are respectively 0.835 and 0.670",
}
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%0 Conference Proceedings
%T NLPZZX at SemEval-2018 Task 1: Using Ensemble Method for Emotion and Sentiment Intensity Determination
%A Zhang, Zhengxin
%A Zhou, Qimin
%A Wu, Hao
%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 zhang-etal-2018-nlpzzx
%X In this paper, we put forward a system that competed at SemEval-2018 Task 1: “Affect in Tweets”. Our system uses a simple yet effective ensemble method which combines several neural network components. We participate in two subtasks for English tweets: EI-reg and V-reg. For two subtasks, different combinations of neural components are examined. For EI-reg, our system achieves an accuracy of 0.727 in Pearson Correlation Coefficient (all instances) and an accuracy of 0.555 in Pearson Correlation Coefficient (0.5-1). For V-reg, the achieved accuracy scores are respectively 0.835 and 0.670
%R 10.18653/v1/S18-1015
%U https://aclanthology.org/S18-1015
%U https://doi.org/10.18653/v1/S18-1015
%P 116-122
Markdown (Informal)
[NLPZZX at SemEval-2018 Task 1: Using Ensemble Method for Emotion and Sentiment Intensity Determination](https://aclanthology.org/S18-1015) (Zhang et al., SemEval 2018)
ACL