@InProceedings{garcialozano-EtAl:2017:SemEval,
  author    = {Garc\'{i}a Lozano, Marianela  and  Lilja, Hanna  and  Tj\"{o}rnhammar, Edward  and  Karasalo, Maja},
  title     = {Mama Edha at SemEval-2017 Task 8: Stance Classification with CNN and Rules},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://www.aclweb.org/anthology/S17-2084}
}