@inproceedings{chen-etal-2017-ikm,
title = "{IKM} at {S}em{E}val-2017 Task 8: Convolutional Neural Networks for stance detection and rumor verification",
author = "Chen, Yi-Chin and
Liu, Zhao-Yang and
Kao, Hung-Yu",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2081",
doi = "10.18653/v1/S17-2081",
pages = "465--469",
abstract = "This paper describes our approach for SemEval-2017 Task 8. We aim at detecting the stance of tweets and determining the veracity of the given rumor. We utilize a convolutional neural network for short text categorization using multiple filter sizes. Our approach beats the baseline classifiers on different event data with good F1 scores. The best of our submitted runs achieves rank 1st among all scores on subtask B.",
}
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<abstract>This paper describes our approach for SemEval-2017 Task 8. We aim at detecting the stance of tweets and determining the veracity of the given rumor. We utilize a convolutional neural network for short text categorization using multiple filter sizes. Our approach beats the baseline classifiers on different event data with good F1 scores. The best of our submitted runs achieves rank 1st among all scores on subtask B.</abstract>
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%0 Conference Proceedings
%T IKM at SemEval-2017 Task 8: Convolutional Neural Networks for stance detection and rumor verification
%A Chen, Yi-Chin
%A Liu, Zhao-Yang
%A Kao, Hung-Yu
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F chen-etal-2017-ikm
%X This paper describes our approach for SemEval-2017 Task 8. We aim at detecting the stance of tweets and determining the veracity of the given rumor. We utilize a convolutional neural network for short text categorization using multiple filter sizes. Our approach beats the baseline classifiers on different event data with good F1 scores. The best of our submitted runs achieves rank 1st among all scores on subtask B.
%R 10.18653/v1/S17-2081
%U https://aclanthology.org/S17-2081
%U https://doi.org/10.18653/v1/S17-2081
%P 465-469
Markdown (Informal)
[IKM at SemEval-2017 Task 8: Convolutional Neural Networks for stance detection and rumor verification](https://aclanthology.org/S17-2081) (Chen et al., SemEval 2017)
ACL