@InProceedings{karpov:2017:SemEval,
  author    = {Karpov, Nikolay},
  title     = {NRU-HSE at SemEval-2017 Task 4: Tweet Quantification Using Deep Learning Architecture},
  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     = {683--688},
  abstract  = {In many areas, such as social science, politics or market research, people need
	to deal with dataset shifting over time. Distribution drift phenomenon usually
	appears in the field of sentiment analysis, when proportions of instances are
	changing over time. In this case, the task is to correctly estimate proportions
	of each sentiment expressed in the set of documents (quantification task).
	Basically, our study was aimed to analyze the effectiveness of a mixture of
	quantification technique with one of deep learning architecture. All the
	techniques are evaluated using the SemEval-2017 Task4 dataset and source code,
	mentioned in this paper and available online in the Python programming
	language. The results of an application of the quantification techniques are
	discussed.},
  url       = {http://www.aclweb.org/anthology/S17-2113}
}