@InProceedings{sorodoc-EtAl:2017:EACLshort,
  author    = {Sorodoc, Ionut  and  Lau, Jey Han  and  Aletras, Nikolaos  and  Baldwin, Timothy},
  title     = {Multimodal Topic Labelling},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {701--706},
  abstract  = {Topics generated by topic models are typically presented as a list of topic
	terms. Automatic topic labelling is the task of generating a succinct label
	that summarises the theme or subject of a topic, with the intention of reducing
	the cognitive load of end-users when interpreting these topics. Traditionally,
	topic label systems focus on a single label modality, e.g. textual labels. In
	this work we propose a multimodal approach to topic labelling using a simple
	feedforward neural network. Given a topic and a candidate image or textual
	label, our method automatically generates a rating for the label, relative to
	the topic. Experiments show that this multimodal approach outperforms
	single-modality topic labelling systems.},
  url       = {http://www.aclweb.org/anthology/E17-2111}
}