@InProceedings{perezestruch-paredespalacios-rosso:2017:RANLP,
  author    = {P\'{e}rez Estruch, Carlos  and  Paredes Palacios, Roberto  and  Rosso, Paolo},
  title     = {Learning Multimodal Gender Profile using Neural Networks},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {577--582},
  abstract  = {Gender identification in social networks is one of the most popular aspects of
	user profile learning. Traditionally it has been linked to author profiling, a
	difficult problem to solve because of the little difference in the use of
	language between genders. This situation has led to the need of taking into
	account other information apart from textual data, favoring the emergence of
	multimodal data. The aim of this paper is to apply neural networks to perform
	data fusion, using an existing multimodal corpus, the NUS-MSS data set, that
	(not only) contains text data, but also image and location information. We
	improved previous results  in terms of macro accuracy (87.8%) obtaining the
	state-of-the-art performance of 91.3%.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_075}
}