@inproceedings{garcia-lozano-etal-2017-mama,
title = "Mama Edha at {S}em{E}val-2017 Task 8: Stance Classification with {CNN} and Rules",
author = {Garc{\'\i}a Lozano, Marianela and
Lilja, Hanna and
Tj{\"o}rnhammar, Edward and
Karasalo, Maja},
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/S17-2084",
doi = "10.18653/v1/S17-2084",
pages = "481--485",
abstract = "For the competition SemEval-2017 we investigated the possibility of performing stance classification (support, deny, query or comment) for messages in Twitter conversation threads related to rumours. Stance classification is interesting since it can provide a basis for rumour veracity assessment. Our ensemble classification approach of combining convolutional neural networks with both automatic rule mining and manually written rules achieved a final accuracy of 74.9{\%} on the competition{'}s test data set for Task 8A. To improve classification we also experimented with data relabeling and using the grammatical structure of the tweet contents for classification.",
}
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<abstract>For the competition SemEval-2017 we investigated the possibility of performing stance classification (support, deny, query or comment) for messages in Twitter conversation threads related to rumours. Stance classification is interesting since it can provide a basis for rumour veracity assessment. Our ensemble classification approach of combining convolutional neural networks with both automatic rule mining and manually written rules achieved a final accuracy of 74.9% on the competition’s test data set for Task 8A. To improve classification we also experimented with data relabeling and using the grammatical structure of the tweet contents for classification.</abstract>
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%0 Conference Proceedings
%T Mama Edha at SemEval-2017 Task 8: Stance Classification with CNN and Rules
%A García Lozano, Marianela
%A Lilja, Hanna
%A Tjörnhammar, Edward
%A Karasalo, Maja
%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 garcia-lozano-etal-2017-mama
%X For the competition SemEval-2017 we investigated the possibility of performing stance classification (support, deny, query or comment) for messages in Twitter conversation threads related to rumours. Stance classification is interesting since it can provide a basis for rumour veracity assessment. Our ensemble classification approach of combining convolutional neural networks with both automatic rule mining and manually written rules achieved a final accuracy of 74.9% on the competition’s test data set for Task 8A. To improve classification we also experimented with data relabeling and using the grammatical structure of the tweet contents for classification.
%R 10.18653/v1/S17-2084
%U https://aclanthology.org/S17-2084
%U https://doi.org/10.18653/v1/S17-2084
%P 481-485
Markdown (Informal)
[Mama Edha at SemEval-2017 Task 8: Stance Classification with CNN and Rules](https://aclanthology.org/S17-2084) (García Lozano et al., SemEval 2017)
ACL