@InProceedings{liu-perez-nowson:2017:EACLlong,
  author    = {Liu, Fei  and  Perez, Julien  and  Nowson, Scott},
  title     = {A Language-independent and Compositional Model for Personality Trait Recognition from Short Texts},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {754--764},
  abstract  = {There have been many attempts at automatically recognising author personality
	traits from text, typically incorporating linguistic features with conventional
	machine learning models, e.g. linear regression or Support Vector Machines. In
	this work, we propose to use deep-learning-based models with atomic features of
	text -- the characters -- to build hierarchical, vectorial word and sentence
	representations for the task of trait inference. On a corpus of tweets, this
	method shows state-of-the-art performance across five traits and three
	languages (English, Spanish and Italian) compared with prior work in author
	profiling. The results, supported by preliminary visualisation work, are
	encouraging for the ability to detect complex human traits.},
  url       = {http://www.aclweb.org/anthology/E17-1071}
}